Spectrum 79 February 2016

§An Outline of Sensor-based Condition Monitoring Techniques

for Hydraulic and Lubricating Fluids In Modern Industrial

Environments

by John K. Duchowski, HYDAC Technology Corporation

§Analysis of the bearing reliability in induction motors.

Dr. D. Chaschin Technical Manager, Rotating Machines Services, ABB Australia

§ Split Mode Balancing for Generator Rotors

by Simon Hurricks

§Test your Knowledge - Part 43 of a Series (pg 37)

Summer 2016 - ISSUE 79

the official journal of the vibrations association of new zealand

this issue:

Spectrum issue 79 - page 2

From process monitoring to vibration analysis

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German made, global solutions.Sensors & control products -

stocked / supported in NZ!

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SPECTRUM

ISSUE 79 – February 2016

ISSN 1173-793X

EDITOR

Angie Hurricks Ph 021 239 4572 Email: [email protected],nz

PUBLISHER

Frans Taris Email: [email protected]

New Zealand Spectrum is published quarterly by the Vibrations Association of New Zealand Inc. The journal is designed to cover all aspects of the vibration field, and is received by all VANZ members including corporate members.

Contributions to Spectrum are welcome.

Please address material to:

Angie Hurricks Spectrum Editor c/o 358 Waerenga Road,

R.D.1, Te Kauwhata, 3781

Waikato, New Zealand

or [email protected]

Statements made or opinions expressed in Spectrum are not necessarily the views of VANZ or its Officers and Committee.

President Jason Tranter Email: [email protected]

Treasurer Graeme Finch Email: [email protected]

Secretary Angie Hurricks Email: [email protected]

Please address all VANZ correspondence to

VANZ

PO Box 2122

Shortland Street

Auckland

Web Site

www.vanz.org.nz


President’s report

The VANZ conference is now just three months away – I hope you have it in your budget and on your calendar!

The 2016 conference will be special. Our keynote speakers, Tod Baer and Mark Gurney, have years of experience developing and managing condition monitoring and reliability improvement programs, and they are coming to Queenstown to share their experiences with you! The theme of their talks could be described as “Inexpensive, practical changes that will reap substantial rewards quickly”!

And when you add to that the Asset Management Awareness Day, the Hands-on Awareness Day, the Dinner/cruise, the Clyde Dam plant tour, the beautiful host city, and a raft of additional excellent, passionate speakers, the 2016 conference will expand your knowledge and send you home with new tips, tricks and techniques that you can apply immediately.

We have a new conference Web-site to make it easier to learn about the conference and register:

www.vanz2016.co.nz

And on that web-site you will find a movie that will explain the format of the conference and introduce some of our speakers.

It has never been more important to learn how to make improvements to save money, reduce downtime, improve safety, and master your chosen profession.

See you in Queenstown!

Regards

VANZ President

Jason Tranter


Angie

A very Happy New Year to all our readers/VANZ members/advertisers!

Here’s hoping the festive season was a chance for a good break with fun, sun and family. We are now gearing up for this years conference with committee members buzzing around like busy bees trying to organise venues, accommodation, presenters and the like. Some of the presenters we have lined up this year include Tod Baer from Minnkota Power, USA, Mark Gurney, ex Murray Goulburn Corp., Australia (via France) and Daré Petreski from Delta Asset Reliability, Australia as well as Clyde Volpe from Vibration Institute of Australia and Matthew Fallow from Asset Quality, Australia so it’s shaping up to be a fascinating schedule with some top presenters who are eager to share their experiences with everyone.

In this issue we have another installation of our Quiz by Carl, read up on the words of wisdom from our

President Jason Tranter and check out the details for our 2016 conference coming up in May!

For registration forms head over to our website www.vanz2016.co.nz and go to our Conference page where you can sign up for either the Awareness Day, Main conference or both! Group registrations also included as well as Trade Stand info so get in quick!

Many thanks to the advertisers who have supported us in this issue being the first 0f 2016, Infratherm, NVMS, Mobius, GV Sensors, Eurotec and IFM Efector, we look forward to working with you this year and appreciate your input.

Prosperous wishes to all for the coming year and enjoy the read!

www.vanz.org.nz

editorial

Library Books Wanted!If you have any books/magazines/documents that you think VANZ

would benefit from having in the collection, please make contact with a

committee member/ Spectrum Editor through contact details. Also if you

have any books/documents borrowed from the VANZ library please take

the opportunity to bring them to the next conference so others can use

the resources.

Help us to keep our library together!


An Outline of Sensor-based Condition Monitoring

Techniques for Hydraulic and Lubricating Fluids In

Modern Industrial Environments

by John K. Duchowski, HYDAC Technology Corporation

The increasingly complex nature of machinery and equipment operating in the present day industrial environments requires the adoption of new and more sophisticated maintenance practices that continuously evolve to meet application requirements. In addition, the role of the industrial plant outfitters and machinery suppliers has shifted from that of “simple” manufacturing and delivery to that of more encompassing equipment availability and reliability providers.

Irrespective of the equipment type involved, machine tools, presses excavators, forklifts, wind, steam or gas turbines, etc., new and innovative marketing, selling and/or leasing approaches that encompass service contracts have been developed and implemented. Frequently, these are not based strictly on the initial selling price but include equipment reliability, availability, operating time span and lifecycle cost all of which have to be taken into account and worked into the technology development programs by equipment and component designers. The most important trends recently gaining ground in equipment and component design considerations as well as their implications for fluid power technology and oil condition monitoring are summarized graphically in Figure 1.

However, the hardware design considerations represent only a part of the picture. This is because equipment operation under field conditions is in large measure dependent on the quality and performance of hydraulic and lubricating oils selected for particular application. In parallel with technological progress in hardware components, these fluids are likewise becoming increasingly specialized in nature and are often tailored to work under very specific operating conditions that include not only hardware requirements but often even climatic conditions. In the course of the last several years, these developments have necessitated a progressive shift towards more complex fluid chemistries both in terms of the chemical structure of the base stocks as well as additive package components.

An aspect that is often overlooked in the discussion on advancements in hardware design and fluid engineering is the increase in cost that is invariably associated with the introduction and implementation of new technologies. In turn, these higher initial capital expenditures in equipment and fluids have put additional pressure on equipment OEMs and end users to improve maintenance and condition monitoring practices to increase the longevity of hardware components and extend fluid service life. In response to these conditions, both the equipment OEMs and the end users have began to turn increasingly towards more sophisticated condition monitoring tools and techniques that predominantly rely on the employment of the in-line or on-line condition monitoring sensors.

2260 City Line Road Bethlehem PA 18052-6117 1-877-GO-HYDAC www.hydacusa.com

Figure 1: OEM Equipment Design Trends and Their Implications.

Figure 2: HYDAC Sensor Array for Condition Monitoring Applications.

Figure 3: Schematic Diagram of the Operating Principles of an Automatic Particle Counter.


continued


The main advantage of in-line or on-line diagnostic devices is that they allow for assessment of fluid condition on an ongoing basis and nearly in real time, in contrast to the more conventional condition monitoring practices that rely principally on analysis of oil samples in remotely located laboratories. Faster access to data derived directly from on-line measurements allows for a better response time and more efficient planning of condition-based maintenance intervals. Although the advantages, desirability and even the necessity for the deployment of on-line condition monitoring sensors are clearly recognized, a number of considerations need to be taken into account prior to the introduction and installation of these devices into industrial environments.

This is because the sensors employed for condition monitoring applications in industrial environments often comprise complicated and delicate electronic components. Consequently, the individual sensing elements need to be able to stand up to the temperatures, pressures and flows commonly found in these environments as well as to aggressive fluids or contaminants present in some applications. In addition, the response signals generated from these sensors have to be collected and transmitted with minimal loss or interference from the surrounding equipment. The challenge then lies in constructing sensors rugged enough to withstand the rigors of the application but cost-effective enough to allow broad deployment throughout an industrial enterprise or a vehicle fleet from the economic standpoint.

The next question to consider is which system parameters and fluid properties need to be monitored in order to provide a reasonably good assessment of system performance and fluid condition. As the latter often contains information reflective of cumulative history of system performance, most of the present-day sensor applications focus on monitoring fluid

properties that can be related to the standard industrial norms and standards from which the current state of the system can be gauged. The data derived from sensor signals can therefore include information on wear processes, cross-contamination due to improper fluid addition and/or leakage, water ingress due to condensation as well as on oxidative fluid degradation caused by excessive temperatures.

For example, monitoring changes in particle counts, water content, fluid acidity and viscosity can provide information on the severity of wear processes, contaminant ingression, leakage through condensation and/or seal failure as well as oil degradation due to ageing or oxidation. Having these parameters on hand then allows for making more informed decisions on scheduling fluid replacement and/or system component maintenance intervals. A proposed sensor array that would allow for monitoring of the typical properties of interest in most hydraulic and lubricating systems is displayed graphically in Figure 2.

This paper summarizes the most recent developments in sensor technologies and provides suggestions for sensor installation in hydraulic and lubricating systems with the aim of arriving at the optimal condition monitoring and predictive maintenance practices in modern industrial environments.






continued


continued

Measurement of Particulate

Contaminant Levels and Determination

of Cleanliness Classes

One of the simplest, yet most informative diagnostic techniques is the on-line particle count. This is so for several reasons. First, the installation and operation of these devices is relatively uncomplicated, which makes them amenable for covering a wide variety of applications. Second, as has been previously reported by numerous literature studies and corroborated by field experience, particulate contamination is often one of the primary causes of accelerated wear, malfunction and even failure of more sensitive system components. Third, by monitoring changes in particulate contaminant levels, it is possible to ascertain the rate of ingression of these contaminants from external sources and/or to determine the onset of accelerated wear, although the delineation of origin of increased particle counts usually requires the use of additional investigative methods, such as microscopy or ferrography. Employment of these additional techniques is required because particle counters cannot distinguish among particles of different composition; they only provide assessment of particle size. Nevertheless, in many cases, particle counts can provide an advanced warning about processes such as gear micropitting or accelerated bearing wear as these often manifest themselves by certain, well-defined changes in particle size distribution. In such cases, changes in particle size distribution often precede detection of macro scale changes, such as those that manifest themselves, for example, in vibration analysis.

Principles of Operation of Optical

Particle Counters

The basic principle of operation of all optical particle counters, whether based on white or monochromatic light (laser or light emitting diode, LED) is light extinction. Basically, this means that when a particle carried by a fluid stream enters the optical path

between the light emitting element (a light bulb, LED or a laser) and the detecting element (usually a PIN diode), the intensity of light is attenuated in proportion to the particle size, or somewhat more accurately, its surface area. Combining this information with the rate of fluid flow (usually fixed and expressed in mL/min) allows to obtain the particle count in terms of the number of particles of given size per unit volume, which can then be translated to the ISO4409 Range Code or the NAS1638 Cleanliness Class. A schematic diagram of the operating principles of a light-blockage automatic particle counter is presented in Figure 3.

The use of these standardized reporting methods has greatly facilitated the exchange of information throughout the industry and placed the technique on much firmer technical ground. For example, it has become standard practice to report particulate contamination levels in terms of the aforementioned ISO4409 Range Codes which summarize the cumulative particle counts for particles in >4 µm(c), >6 µm(c) and >14 µm(c) size ranges where (c) denotes the use of a calibration contaminant (ISO Medium Test Dust, or ISO MTD) traceable to the National Institute of Standards





Figure 3: Schematic Diagram of the Operating Principles of an Automatic Particle Counter


and Technology (NIST) and defined by the ISO12103-1, A3 international standard. Likewise, the use of internal diagnostics and calibration standards specific to the nature of the application, such as the ISO11943 for referencing on-line measurements, the use of on-line particle counters has become a widely recognized and well accepted industrial practice. An illustration of on-line particle data collected in the course of an excavator work cycle is depicted graphically in Figure 4.

Finally, advancements in signal processing and software have made these devices much simpler to operate and the data they generate much simpler to interpret.

Depending on the nature of the application at hand, particle counters can be installed in pressure lines (preferably), reservoirs or return lines, provided that the latter are not subject to aeration and hence interference from air bubbles. On-line installation of these devices offers several advantages compared to conventional techniques of bottle sample analysis at remote laboratories. First, the on-line count is significantly more accurate (and usually lower by about one to two ISO Code Ranges, especially in clean systems) than bottle sampling which suffers from cross contamination effects introduced in the course of obtaining and subsequent handling of the sample. Second, continuing developments in electronics

and optics technologies have contributed greatly to improved accuracy, wider dynamic range and faster response time of these devices. Future developments are likely to include a more sophisticated manipulation of such parameters as the LED intensity and draw current in order to overcome the present day difficulties of employing optical particle counters for on-line measurements that involve opaque, translucent or inhomogeneous (water- or air-containing) fluids, which prevent a more wide spread application of these devices in a broader scope of applications.

Historical Overview of Technological

Developments in Particle Counting

Technologies

Fluid Control Unit 2000. HYDAC Technology Corporation introduced its first rugged, portable optical particle counter designed specifically for field use in industrial applications about ten years ago. The Fluid Control Unit 2000 (or FCU 2000) was capable of handling both low and high pressure application (up to 450 bar or 6,500 psi) and was outfitted with minimess sampling ports for easy and leak-free connection to system sampling ports with minimal disruption to system operation. The unit came equipped with a keyboard, an LCD display and a printer which provided the means for data input, display and logging of input parameters and output data. The FCU 2000 is displayed in a photograph shown in Figure 5.

Although devices built upon newer technology platforms have since been introduced, the FCU 2000 continues to be a useful and necessary tool for on-site evaluations where precise measurements of particulate

Figure 4: On-line Particle Count Data Collected in the Course of an Excavator Work Cycle.

Figure 5. The FCU 2000 Particle Counter: a Useful Tool for On-site Evaluations of Particulate Contaminant Levels

continued on page 11


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contaminant levels are required but only a minimal disruption and/or temporary connection to the operating system is possible.

Contamination Sensor 2000. The Contamination Sensor 2000 (or CS 2000) represents the next step in the development of particle counter technology. It was introduced about four years ago and targeted principally at permanent, stationary or in-plant installations. In addition to the particle sensor, the unit has been outfitted with various analog, switching and computer outputs which make it particularly amenable for installation on test benches, filling stations or other in-plant or equipment where it can be used for continuous monitoring of contaminant levels under normal operating conditions and/or in the course of filling or flushing operations. The CS 2000 is displayed in a photograph shown in Figure 6.

An example of a permanent installation of the CS 2000 contaminant sensors for monitoring of contaminant levels in large hydraulic systems is depicted in Figure 7.

Contamination Sensor 1000. The Contamination Sensor 1000 (or CS 1000) is the latest addition to the HYDAC line of on-line particle counters. Employing the latest in electronic and optical technologies, the CS 1000 features a significantly reduced footprint (50% smaller compared to the CS 2000). For example, care has been taken to incorporate circuitry to provide protection against load dump transients and ensure electromagnetic compatibility (EMC). In addition, the durable aluminum construction, as well as the IP 69K enclosure rating, make the CS 1000 vibration resistant and impervious to ingress of dust and effects of high-pressure jet steam cleaning and thus particularly attractive for installation on mobile equipment.

The CS 1000 is available with or without display and is outfitted with analog and switching output interfaces. In addition to monitoring particulate contaminant levels, the unit reports system temperature via the integrated RTD sensor. Similarly to the other contaminant sensor in the HYDAC line, the unit is capable of handling system pressure of up to 450 bar (6,500 psi). Its cost-optimized construction and its features make it suitable for permanent installation in almost any hydraulic or lubricating system. The innovative design and combination of features make the CS 1000 one of the most advanced contamination monitors on the market today and, consequently, make it amenable to the exploration of entirely new fields of application in the area of on-line contamination measurements. The CS 1000 is displayed in a photograph shown in Figure 8.

Figure 6. The CS 2000 Contamination Monitor for Continuous Monitoring of Contaminant Levels Under Normal Operating Conditions.

Figure 7. Permanent Installation of the CS 2000 Contaminant Sensors for Monitoring Contaminant Levels in Large Hydraulic Systems.

Figure 8.The CS 1000 – Advanced Contamination Monitor for Permanent Installation in Hydraulic and Lubricating Systems.

continued


Effects and Behavior of Water in

Hydraulic and Lubricating Fluids

Water is one of the most pervasive, most common and most damaging of chemical contaminants that threaten the operation and performance of hydraulic and lubricating systems. In addition to its impact on the physical properties of the fluid, such as viscosity loss and therefore diminished load carrying capacity, water may interact chemically either with the base fluid itself, or with the components of the additive package. Its adverse impact on system and fluid performance manifests itself through such processes as hydrogen embrittlement and/or corrosion of system components and hydrolysis of the fluid or additive package components. Yet water remains one of the most cumbersome contaminants to deal with, owing chiefly to the difficulties associated with its detection in a quantitative manner by the instruments and techniques currently available on the market. This is especially true in case of the devices intended for continuous, on-line monitoring of water content in industrial applications. Nevertheless, some significant progress in this area has been made, particularly in recent years.

Part of the difficulty of detecting water in hydraulic and lubricating fluids is that, depending on the nature of the fluid base stock and the additive package components, water in these fluids may exist in several different states, each of which requires a different detection technique. Thus at low concentrations, water remains completely dissolved in the host fluid and forms with it a homogeneous solution. The oil appears transparent to the naked eye and the presence of water can only be detected with the aid of suitable analytical techniques. At higher concentrations, water might exist in the form of droplets dispersed throughout the host fluid in the state of an emulsion. The fluid then appears cloudy or even opaque although the bulk phase separation has not yet taken place.

Finally, at still higher concentrations, when water is present in sufficient quantities to allow for coalescence of dispersed droplets, a complete phase separation occurs as a result of droplet recombination. Water is then said to exist in a “free” state where the term refers to the water phase completely separated from the oil phase. Depending on the differences in fluid densities, the water phase might then settle to the bottom of the tank, as is the case with either synthetic or mineral hydrocarbon based fluids, or migrate to the top of the tank, as with phosphate esters.

Determination of Water Content in

Hydraulic and Lubricating Fluids with

On-line Sensors

A great majority of the on-line sensors employed today for the determination and continuous monitoring of water content in hydraulic and lubricating systems rely on capacitance measurement in one form or another. This is because the capacitor sensing elements typically employed for these applications are inexpensive and robust which makes them suitable for operation in a wide variety of different fluid types and system conditions. In addition, the fact that similar sensors were previously employed for determination of humidity in air, made their “transplantation” to applications involving other fluids relatively simple. These sensors employ a capacitor with a polymer dielectric, sandwiched between metal electrodes. This assembly is then encapsulated by a ceramic substrate for additional durability. The relative humidity reading is obtained when the water in the surrounding medium reaches equilibrium with the water that has migrated into the dielectric which increases the capacitance of the sensor. The temperature compensation of capacitance readings is accomplished by incorporating an RTD temperature sensor next to the humidity sensor in probe body.

Aqua Sensor 2000. As with particle sensors, one of the predominant development trends in water

continued


sensor technology leads towards miniaturization. For example, the HYDAC Aqua Sensor 2000 (or AS 2000) developed about three years ago comprised a probe and a separate display console which housed the signal acquisition and data processing electronics. In addition, the console was outfitted with an LED display panel and control buttons for sensor set up and data navigation. The sensor was suitable for both permanent installation in the oil supply and return lines in existing systems, as well as for portable use as a diagnostic device for verification of water content, for example, in the reservoirs. The AS 2000 is displayed in a photograph shown in Figure 9.

An example of the AS 2000 installation for monitoring water content in lubricating oil of a dry end of a paper machine is shown in Figure 10.

An example of a combined installation of the CS 2000 contaminant sensors and AS 2000 water sensors for on-line monitoring of contaminant levels in a press cushion hydraulic system is depicted in Figure 11.

Aqua Sensor 1000. A new development in the HYDAC water sensor line is represented by the Aqua Sensor 1000 (or AS 1000). Greatly reduced in size and with the signal processing electronics incorporated in a robust aluminum housing, the AS 1000 is particularly

suitable for OEM installation on new equipment. Both the AS 2000 and AS 1000 can handle pressures of up to 50 bar (725 psi) and therefore lend themselves for installation in oil supply and return lines in most hydraulic and lubricating systems. Although reservoir installation is also possible, the sensor response time is greatly improved when the sensing element is in direct contact with the oil flow. The AS 1000 is displayed in a photograph shown in Figure 12.

Aqua Sensor 8000. Whereas the AS 2000 and AS 1000 sensors provide information on the dissolved water content, the measurement of free water content required certain modifications in the design of the sensing capacitor. In particular, the solid state, polymer-based dielectric sensor had to be replaced with a capacitor of tubular design to allow for measurements on bulk fluid in the course of its flow through the sensing zone. In this adaptation, the fluid flows axially through the sensing zone where the oil itself acts as the dielectric medium. The presence of the free water in the sensing zone changes the capacitance value and is then correlated to the free water content in bulk oil. The Aqua Sensor 8000 (or the AS 8000) represents the latest addition

Figure 10. An Example of the AS 2000 Water Sensor Installation for Monitoring Water Content in the Lubricating System on a Dry End of a Paper Machine.

Figure 11. Combined Installation of the CS 2000 and AS 2000 Sensors for Monitoring of Contaminant Levels in a Press Cushion Hydraulic System.

Figure 12.With tGreatly Reduced Size and Incorporated Signal Processing the AS 1000 is Particularly Suitable for OEM Installations.

continued

Figure 9.

The AS 2000 Water Sensor Featuring a Probe and Separate Display Console.


to the HYDAC water sensor line and is intended predominantly for installation in return lines where the presence of free water is of concern, such as those on the wet-end of paper machine lubricating systems in paper mills or on the Morgoil systems in steel plants. The AS 8000 is displayed in a photograph shown in Figure 13.

Oil Condition Monitoring

with the HYDACLab® Sensor

The HYDACLab oil condition monitoring sensor represents the latest and most sophisticated addition to the HYDAC sensor line. The HYDACLab comprises four individual sensing elements in the sensor probe which allow for simultaneous measurement of temperature, relative humidity, changes in viscosity and the dielectric constant. Although the measurement of variations in viscosity and the dielectric constant is not absolute and needs to be referenced against the initial values obtained for contaminant-free and unused oil, it nevertheless provides a reliable indication of the overall oil condition.

In general, oil viscosity and oil polarity increase with age as a result of exposure to physical stress, thermal (oxidative) degradation or hydrolytic breakdown. These processes often lead to the formation of acidic byproducts and polymerization. An example of fluid

breakdown processes leading to sludge formation are depicted on a photograph shown in Figure 14.

Consequently, tracking changes in oil viscosity and the dielectric constant can be used for determination of suitability of the fluid for continued operation and/or for scheduling fluid replacement intervals that are linked to fluid condition. In addition to tracking changes associated with the ageing process, HYDACLab may also be effective in identifying fluid cross contamination arising as a result of top-offs with improper oils and/or ingression of process fluids into the hydraulic or lubricating systems. This is because such mixing effects will also be reflected by wholesale changes in fluid viscosity and the dielectric constant.

The HYDACLab sensor therefore forms and integral part of such condition-based maintenance programs in which it often plays a central role. A judicious use, proper interpretation and trending of the HYDACLab data, can therefore result in considerable savings in equipment maintenance and plant operation due to the elimination of unplanned shutdowns, unnecessary fluid replacements and early prevention of potential fluid related problems. The HYDACLab sensor facilitates data acquisition, storage and trending by providing the operator with analog signals that can also activate switching outputs through the employment of triggering mechanisms. The HYDACLab sensor is displayed in a photograph shown in Figure 15.

Differential Pressure (DP) Transducers

as Condition Monitoring Tools: the

DirtController GW

The use of the DP transducers for condition monitoring practices is quite often overlooked. Yet the data they

Figure 14. Formation of Acidic Byproducts and Polymerization as a Result of Fluid Breakdown Eventually Leading to Sludge Formation.

Figure 15. The HYDACLab Condition Monitoring Sensor for Simultaneous Measurement of Temperature, Relative Humidity, Viscosity and Dielectric Constant

continued

Figure 13. The AS 8000 Water Sensor for Measurements of Free Water in the Bulk Fluid


provide contains valuable information about events that can signal impending or even catastrophic system component failures or upsets in fluid condition. This is because in well-behaved and stable systems, the rise of the DP across the filter element exhibits a well-defined and nearly exponential progression in time. If the system remains stable, the filter element service life usually demonstrates a similarly stable behavior with the successive DP vs time curves exhibiting nearly identical trends from element to element. However, should any significant changes in contaminant levels or nature develop within the system, the DP curve can prove to be a highly valuable source of information from which the origin of these events can be determined. For example, an earlier onset or a steeper rise of the exponential curve is usually indicative of a significant increase in contaminant levels, whereas a change in the shape of the curve, for example from exponential to linear, may be indicative of a different filter loading mechanism (usually caking) usually associated with a change in contaminant nature or appearance of a new contaminant.

In recognition of the importance and benefit of employing DP transducers for continuous monitoring of filter differential pressure, HYDAC has developed a new DP transducer, the DirtController GW. The DirtController GW comprises two electronic pressure sensors that provide simultaneous acquisition and processing of signals that correspond to the system (inlet pressure) and the differential pressure across the filter element, respectively. In addition, with the help of the analog and switching outputs, triggers that correspond to the by-pass cracking pressure can also be set. A particularly interesting feature of the sensor is that it also recognizes whether a filter element or even whether an appropriate filter element has been installed in the filter housing. In summary, the employment of the DirtController GW can provide the system operator with important information on system stability, operating parameters and internal

processes that can affect system performance thereby making the sensor an indispensable component of predictive maintenance programs. The DirtController GW transducer is displayed in a photograph shown in Figure 16.

Employment of Data Acquisition and

Processing Controllers in Predictive

Maintenance Programs

The latest developments in sensor technology described herein represent significant advances in the fields of contamination control and condition monitoring. In addition to covering a wide range of existing applications, these latest developments open many new areas of application as well as pave the way for broadening the scope of the current predictive maintenance programs.

An equally interesting and challenging aspect of condition monitoring programs, if they are to achieve a greater measure of sophistication as well as usefulness, is that of signal acquisition and data processing that need to be performed to facilitate the interpretation of the information provided from sensor outputs. Moreover, in many cases, by combining and superimposing the data generated by various individual sensors can often reveal trends that might have otherwise been missed if the sensor signals were interpreted in standalone manner. In

Figure 16. The DirtController GW with Two Electronic Pressure Sensors for Simultaneous Acquisition of System and Differential Pressure.

continued


contrast, combined sensor data usually presents a far more comprehensive picture of system performance, component operation and fluid condition which puts the decision-making process on much firmer technical ground. A proposed remote monitoring and data acquisition system is depicted graphically in Figure 17.

In order for maintenance programs to achieve this level of sophistication, the data collected from the distributed arrays of dissimilar sensors, which might or should include particle counters, water and differential pressure sensors, needs to be collected and sent to a centralized location where it can be stored and presented in a user-friendly manner. This requires that the sensor output signals, analog or digital, be converted and transmitted with the aid of industry-accepted communication protocols for later interpretation with appropriate software tools.

Ideally, in addition to remote monitoring and data acquisition, a sophisticated diagnostic system should also be able to trigger alarms that can be transmitted via email or cellular telephone in order to allow the operator to take appropriate remedial action in response to upset conditions reported by one or more sensors. In anticipation of these developments, HYDAC has recently introduced two new data acquisition units to augment the existing HMG line.

HMG 500 Data Acquisition Unit. The smaller, hand-held HMG 500 unit can simultaneously acquire and display data from up to two sensors, regardless of sensor or signal type. For example the analog current and voltage signals (4 to 20 mA or 0 to 10 V) can be simultaneously acquired and displayed. In addition, the data can be displayed in the form of independent individual values or in the form of a differential reading between the two sensors. The HMG 500 is displayed in a photograph shown in Figure 18.

HMG 3000 Data Acquisition Unit. The larger HMG 3000 is a sophisticated, high-end portable data acquisition unit, about the size of a portable oscilloscope. It can simultaneously acquire and display data from up to ten sensors, again regardless of sensor or signal type. In addition to analog inputs, the HMG 3000 has also been outfitted with two ports that accept digital input for acquisition of speed or frequency data. The unit features a high-resolution color display capable of displaying data in graphical or numeric format. It also offers unparalleled data storage capacity capable

Figure 18.

The Hand-held HMG 500 Data Acquisition Unit for Simultaneous Acquisition and Display of Analog Data from Two Sensor Elements

continued

Figure 17.

A Proposed Remote Monitoring and Data Acquisition System.


of storing of up to 50 measurements curves, each of which can comprise of up to 500,000 individual values. In addition to data, the unit can be programmed with user-specific profiles that hold machine and/or measurement related information, thereby greatly facilitating the instrument set-up process. Data stored in the HMG 3000 can be downloaded to a computer equipped with the HMGWIN software via a built-in USB port. The HMG 3000 is displayed in a photograph shown in Figure 19 on the following page.

HYDAC Sensor Interface. Both HMG 500 and HMG 3000 data acquisition units are equipped with a proprietary HYDAC Sensor Interface (HIS) that enables automatic recognition of temperature, pressure, humidity, flow and HYDACLab sensors in the HYDAC product line. Communication with equipment predating the development of the HSI, such as the CS 2000 contamination sensor or older computer types, can be accomplished with the HMG 3000 unit via the built in serial interface.

Summary

Condition-based maintenance programs have become a recognized and established practice in the present-day industrial world. Their value in optimizing system performance, extending component and fluid service life has been demonstrated and acknowledged by numerous studies and verified by field experience. With advancements in electronics and software, the range of applications for on-line sensors continues to increase while their costs continue to decrease. As these trends are expected to continue, they will open new avenues for sensor implementation and lead to further enhancements of condition monitoring programs and maintenance practices.

The information on the latest developments in sensor and condition monitoring technologies presented in this paper should be helpful to personnel involved in predictive maintenance and condition monitoring programs at industrial facilities of various branches. It may also serve as a guideline as to which sensor components might be useful when instituting new programs or augmenting existing ones. Continued innovation in these fields will raise the level of sophistication in these important endeavors and continue to contribute to further reduction in operating and maintenance costs.

For additional information please contact:

HYDAC Technology Corporation

2260 City Line Road

Bethlehem PA 18052-6117

1-877-GO-HYDAC

www.hydacusa.com

Figure 19. The Hand-held HMG 300 Data Acquisition Unit for Simultaneous Acquisition and Display of Analog Data with Ten Analog and Two Digital Inputs.

http://www.hydacusa.com


27th VANZconference2016 : Queenstown, NZ : May 10-12, 2016

Conference topics focus on reliability improvement, condition monitoring and vibration analysis and range from machine fault diagnostics to best practices in the implementation of a condition monitoring or reliability improvement program. Our conference feedback is always very positive because attendees take away relevant new understanding and skills.

Spectrum issue 79 - page 22continued

Vibration monitoring

Loose foot, unbalance

Misalignment

efector octavis range of products

Early recognition of vibration changes and avoidance of consequential damage

Why is vibration monitoring necessary?

All machines are subject to vibrations.

For example machine unbalance,

misalignment and resonances can cause

machines to vibrate above an acceptable

level. A rise in vibrations is detrimental to

machine health. This results in unexpected

machine failure and reduced availability.

The solution with efector octavis:

Overall vibration velocity is used in

industry standards to evaluate the overall

machine condition. Recommendations for

switching thresholds are given in

ISO 10816. All ifm vibration sensors

conform to the ISO 10816 standard.

efector octavis detects the occurrence of

potential damage at an early stage. Machine vibration trend according to ISO 10816

Time

Vib

rati

on

vel

oci

ty

Alarm

Warning

Monitoring vibration velocity.The vibration switch VK monitors online the overall condition of machines and equipment according to ISO 10816. The sensor measures the rms values of overall vibration and signals when vibration levels are too high.

Early recognition of unbalance.Due to unbalance or misalignment conditions permissible machine vibrations can rapidly exceed allowable levels. The result is unexpected downtime and reduced availability. With sensor type VN it is possible to continually monitor, display and document vibrations over 120 rpm.

Monitor up to 4 measurement points.Using the accelerometers type VSA, you can measure machine vibrations in difficult to access in locations. With the diagnostic electronics type VSE you can measure and document up to 4 measurement points. Ethernet interface enables integration into networks for remote diagnostics.

Vibration sensors type VSA/VSPRobust accelerometers VSA or intrinsically safe sensors VSP, for connection to diagnostic electronics VSE.

Intelligent vibration switch type VNLocal display, onboard time-stamped history function.

Basic vibration switch type VKSwitching output and analogue function. Response delay to avoid triggers during run-up.

Basic vibration transmitter type VTSimple transmitter function 4…20 mA.

Diagnostic electronics type VSECabinet mounting, 4-channel diagnostic module with additional inputs for process values, onboard history function and analysis, suitable for networking.

ifm

efe

cto

r o

ctav

is –

NZ

Ph

: 080

0 80

3 44

4 w

ww

.ifm

.co

m/n

z


Analysis of the bearing reliability in induction motors.

Dr. D. Chaschin Technical Manager

Rotating Machines Services ABB Australia

1. Introduction

The most common understanding of reliability is time before the failure. The longer machine is operating the more reliable it is considered. It is quite arguable but let us stick to this definition for the purpose of this article. Time before the failure of the electric motor is the function of many variables such as quality of materials, quality of the manufacturing process, quality of power supply, machine set up, load characteristics etc. The fact that these variables are mostly random makes the time to the failure the random value as well. As all random values, the time to the failure can be described by probability function and statistically assessed using mean value and deviation. When we are talking about technical objects, the deviations of the controlled variables could be represented by their tolerances. Obviously, the tighter are the tolerances the higher the quality of the components. This will allow us to connect the quality of the components with the time to failure. Because the variations of the parameters are limited by the tolerances and in fact or at least should be reasonably small, we can use comparatively easy mathematical tools to find the connection between the quality of the components and the reliability of the motor.

2. Mathematical Model We can express the time to failure as a function like follows: T = f(X1, X2, X3…Xi….Xn), (1) Where T is the time to failure and Xi is the variables, that effect this time to failure according to the function f. But what we are really interested in is how the quality of the motor components and operational conditions effect the variation of the time to failure or, in other words, how the variations of the parameters ΔXi effect the variation of the time to failure ΔT. ΔT = f(ΔX1, ΔX2, ΔX3… ΔXi…ΔXn) (2) The function f is nonlinear and can be described using Taylor Series:

( ) ...))((!1...))((

!31)("

!21))((')()( 00

)(300

)3(200000

nn xxxfn

xxxfxxxfxxxfxfxf −++−+−+−+= (3)

But if we agreed, that the variations of the parameters Xi are small, so the functions f can be considered linear within the variations of X, we can neglect the derivatives of order higher than one. Then we can rewrite the equation (3) as follows:

Analysis of the bearing reliability in induction

motors.

Dr. D. ChaschinTechnical Manager, Rotating Machines Services, ABB Australia

1. Introduction

The most common understanding of reliability is time before the failure. The longer machine is operating the more reliable it is considered. It is quite arguable but let us stick to this definition for the purpose of this article.Time before the failure of the electric motor is the function of many variables such as quality of materials, quality of the manufacturing process, quality of power supply, machine set up, load characteristics etc. The fact that these variables are mostly random makes the time to the failure the random value as well.As all random values, the time to the failure can be described by probability function and statistically assessed using mean value and deviation.

When we are talking about technical objects, the deviations of the controlled variables could be represented by their tolerances. Obviously, the tighter are the tolerances the higher the quality of the components. This will allow us to connect the quality of the components with the time to failure.Because the variations of the parameters are limited by the tolerances and in fact or at least should be reasonably small, we can use comparatively easy mathematical tools to find the connection between the quality of the components and the reliability of the motor.

2. Mathematical Model

continued


))((')()( 000 xxxfxfxf −=− (4)

Here )()( 0xfxf − - the function variation;

0xx − - the argument variation. With some alterations we can rewrite (4) as follows:

)()('

)()()(

0

0

0

00

0

0

xfx

xxxxf

xfxfxf −

=− (5)

Here )()()(

0

0

xfxfxf − - relative function variation;

0

0

xxx − - relative argument variation.

For several variables (5) looks like this:

xx

xxxxx

xxxxx

i

i

ni

i

i

nin

ff

xfxf Δ

∂∑

⎥⎥⎦

⎤

⎢⎢⎣

⎡•

∂=

Δ),...,...,(

),...,...,()()(

21

211

0

(6)

Or in terms of time to failure:

xx

xxxxx

xxxxx

i

i

ni

i

i

nin

o ff

TT Δ

∂∑

⎥⎥⎦

⎤

⎢⎢⎣

⎡•

∂=

Δ),...,...,(

),...,...,(

21

211

(7)

The expression in the square brackets shows the sensitivity of the time to failure variation ΔT to the deviation of each parameter ΔXi.

3. Structure of the problem The easiest way of analysis of the complex problem is to subdivide it in the smaller parts. For example the reliability of the ball bearings in induction motor can be presented as following diagram (Fig 1). Here the problem of the bearing reliability is subdivided in four levels. It is called the Target Tree. First level (A) – the main target – bearing reliability. It can be achieved through the second level (B) – main parameters. The main parameters are disintegrated to the elements level (C). Almost each element can be influenced by primary factors, shown on level D. The lines on the diagram are showing the connections between the elements of different levels. For example motor power (D17) effects the load transmission force (C11), which is part of bearing load (B2) and through this path effects the main target.

))((')()( 000 xxxfxfxf −=− (4)

Here )()( 0xfxf − - the function variation;

0xx − - the argument variation. With some alterations we can rewrite (4) as follows:

)()('

)()()(

0

0

0

00

0

0

xfx

xxxxf

xfxfxf −

=− (5)

Here )()()(

0

0

xfxfxf − - relative function variation;

0

0

xxx − - relative argument variation.

For several variables (5) looks like this:

xx

xxxxx

xxxxx

i

i

ni

i

i

nin

ff

xfxf Δ

∂∑

⎥⎥⎦

⎤

⎢⎢⎣

⎡•

∂=

Δ),...,...,(

),...,...,()()(

21

211

0

(6)

Or in terms of time to failure:

xx

xxxxx

xxxxx

i

i

ni

i

i

nin

o ff

TT Δ

∂∑

⎥⎥⎦

⎤

⎢⎢⎣

⎡•

∂=

Δ),...,...,(

),...,...,(

21

211

(7)

The expression in the square brackets shows the sensitivity of the time to failure variation ΔT to the deviation of each parameter ΔXi.

3. Structure of the problem The easiest way of analysis of the complex problem is to subdivide it in the smaller parts. For example the reliability of the ball bearings in induction motor can be presented as following diagram (Fig 1). Here the problem of the bearing reliability is subdivided in four levels. It is called the Target Tree. First level (A) – the main target – bearing reliability. It can be achieved through the second level (B) – main parameters. The main parameters are disintegrated to the elements level (C). Almost each element can be influenced by primary factors, shown on level D. The lines on the diagram are showing the connections between the elements of different levels. For example motor power (D17) effects the load transmission force (C11), which is part of bearing load (B2) and through this path effects the main target.

3. Structure of the problem

continued


We can distinguish three different groups in the level D: - Motor manufacturing factors (D1, D2, D3, D4, D5, D7, D8, D14, D17, D18); - Bearing manufacturing quality (D6, D9, D10, D12, D13); - Motor set up and operation (D11, D15, D16, D19).

4. Problem solution

In statistical terms we can interpret this problem as follows. As a random value the time before failure for the bearings can be presented as probability functions. It is known that the time before failure for rolling bearings is described by Weibull distribution:

=)(tF {0 if t< a (8)

Here a – location parameter; b – scale parameter and k – shape parameter. For ball bearings shape parameter k = 1.5. Then scale parameter for 90% probability can be calculated as:

10905.1

90 48.448.4)100/90ln()100/ln(

Ltttb

k==

−=

−=

γγ (9)

Here 10L - basic rating life of the bearing used in the most of the bearing hand books. Now the Reliability function of the ball bearing can be presented as:

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−=

5.1

1048.4exp)(

LttR (10)

Here t- time of operation, hours; L10 – basic rating life of the ball bearing, hours. Probability density function of the Weibull distribution is:

( )k

Latk

eLat

Lktf

⎟⎟⎠

⎞⎜⎜⎝

⎛ −−

−

⎟⎟⎠

⎞⎜⎜⎝

⎛ −= 1048.4

1

1010 (11)

Figure 2 shows the graph of the function (11) when a=0 (operation starts at t=0 hours) and L10 = 5000 hours (red) and 10000 hours (blue).

)(1)(

atife bat k

≥−−

−

4. Problem solution

continued


Level A

Level B

Level C

Level D

1.Dynamic load rating

2.Bearing load 3.Lubrication

1.N

umbe

r of

rol

ling

elem

ents

2.C

hara

cte

ristic

s of

m

ater

ial

3.C

onta

ct

angl

e

4.S

ize

of

the

rolli

ng

elem

ents

5.S

quee

zing

for

ces

6.V

ibra

tion

load

Bearing reliability

7.U

nbal

lan

ce lo

ad

8.R

otor

w

eigh

t

9.R

otor

w

eigh

t

11.L

oad

tran

smis

sion

for

ce

12.S

hock

lo

ad

13.L

ubric

atin

g la

yer

thic

knes

s

14.L

ubric

ant

wor

k ca

paci

ty

1.B

earin

g jo

rnal

dia

met

er

2.B

earin

g jo

rnal

tol

eran

ce

10.M

agne

tic

pull

14.E

ccen

tric

ity o

f th

e ai

r ga

p

4.B

alan

ce q

ualit

y

5.Le

ngth

of

the

air

gap

6.B

earin

g rin

gs s

hape

7.B

earin

g al

ignm

ent

17.M

otor

pow

er

8.B

earin

g jo

rnal

run

out

9.B

earin

g cl

eara

nce

10.B

earin

g bo

re d

iam

eter

3.B

earin

g ra

ce r

adiu

ses

12.B

earin

g ra

ce

undu

latio

n

13.B

earin

g ra

ce

roug

hnes

s

18.L

ubric

ant

cons

iste

ncy

15.M

otor

cou

plin

g

16.L

oad

char

acte

ristic

s

19.L

ubric

ant

tem

pera

ture

11.R

adia

l tem

pera

ture

gr

adie

nt

Fig.1 Target Tree of the Bearing reliability

continued


0

0.00005

0.0001

0.00015

0.0002

0.00025

05000100001500020000250003000035000400004500050000550006000065000700007500080000850009000095000

100000

Time

Figure 2. Probability density distribution of roller bearings

The area under the curves shows percentage of the bearings that would fail before the time T. For example the hatched area on the fig.2 shows percentage of the failed bearings before 25000 hours of operation. As you can see, the percentage of the area under the 10000 hours curve is much smaller than percentage under the area of 5000 hours curve. This simply means that usage of the bearings with L10 = 5000 hours instead of bearings with L10 = 10000 hours for the same load will reduce their chance to operate without a failure for 25000 hours. This means that by using bearings with higher basic rating life we increase their reliability. This is quite common way of solving the problem. Now let’s try to answer the question: Which bearing is more reliable, the one that has basic rating life of 5000 hours or the one that has 10000 hours? Immediate reaction would be to say 10000 hours. But if we have a look at the graphs above we will see that there is still a substantial chance for the bearing with basic life of 10000 hours to fail. So I will rephrase the question. Which bearing would you prefer the one that will fail SOME TIME between 0 and 25000 hours of operation or the one that would fail between 10000 and 12000 hours? I think I would prefer the last one. Although it wouldn’t work for 25000 hours, but knowing that it is going to fail after 10000 hours would gave me enough time to prepare for the overhaul. So in most cases the problem of reliability is not in early break but in unexpected one. As we said before, the reason of variation of the time before failure is in large quantity of the variables that are unknown during the operation. Is it possible to reduce the number of unknown factors? To answer this question let’s go back to the model structure. There are too many formulae connecting levels D and A, to show in this paper, but the basic one is:

mh

th QC

nKKL ⎟⎟

⎠

⎞⎜⎜⎝

⎛=

60106

λ (12)

Here Lh – basic rating life, hours; n – motor RPM; Ch – basic dynamic load rating, N; Q – dynamic load, applied to the bearing during the operation time, N;

L10=5000 hr L10=10000 hr

continued


KtK ,λ - corrective coefficients depending on the lubrication conditions and temperature, m – bearing type factor equal to 3 for ball bearings and 10/3 for roller bearings. Using (12) as the function in (7), we can calculate the sensitivity of the bearing basic life variation to the variations of the load Q and basic dynamic load rating Ch. After doing the math we get the sensitivity of the Lh to Ch ,Kc=m, and sensitivity of Lh to Q, QK =-m. This means for example for the ball bearing (m=3) that increasing of the basic dynamic load rating by 10% will cause the increase of the basic rating life by 3*10%=30%. “Minus” sign in front of QK means that 10% increase of the load will cause 30% decrease of the bearing life. But we have to remember, that (7) works only when the deviations are small. Using (7) for large deviations can cause substantial errors. Similar calculations can be done for the levels C and D. These calculations were performed using the data for 7.5 kW induction motors. As a result the share of each group of primary factors in total dispersion of bearing life time was calculated. The results are as follows: Motor manufacturing factors – 17% Bearing quality factors - 30% Operational conditions - 53%. It is very unfortunate distribution because it gives around 50% of the bearing life variation to the factors we can’t control when we receive the motor. This means that no matter how good we can set up the motor we will still have substantial risk of the bearing failure. In these circumstances the only logical step would be the implementing of the motor benchmarking and condition monitoring. Application of the modern methods of diagnostics can determine quite reliably all possible deviation that would affect the bearing life in operation and shown on level C. Received results also show how important it is to KNOW what conditions your motor is subject to from the start of operation. The following example shows how simple monitoring of the belt tension would affect the reliability of the bearing.

5. Example

Induction motor is driving a fan through the pulley and set of v-belts. The DE bearing is 6305. The other parameters are as follows: Basic dynamic load rating C = 22500 N; Mean value of the dynamic load P = 1000 N; Rotation speed n = 1500 RPM; Belt tension force Pb = 400 N; force variation from 100 to 700 N Magnetic pull force Pm = 150 N; force variation from 0 to 300 N All other components (fan load, contact angle of the bearing, temperature, lubrication characteristics etc.) are presumed stable.

continued

5. Example


1. Variation of the tension force: 700-100 = 600 N 2. Estimation of the standard deviation: 600/6 = 100 N 3. Variation of the magnetic pull: 300-0 = 300 N 4. Estimation of standard deviation: 300/6 = 50 N

5. Resultant variation 5010022

+ = 111.8 N 6. Estimated mean life expectancy according to Lundberg/Palmgren bearing

equation (12):

=⋅= ⎟⎠

⎞⎜⎝

⎛PC

nKtKL

36

10 6010

λ 1*1*106/60/1500*(22500/1000)^3 =

12656 h 7. Standard deviation in per load units: 111.8/1000 = 0.1118 8. Sensitivity of the time to failure to the variation of the load according to (7)

QK = -3 9. Deviation of the time to failure because of the load variation:

3*0.1118 = 0.3354 10. Deviation of the time to failure in hours: 12656*0.3354 = 4245 h 11. Shape parameter of the Weibull distribution for LL M/σ =0.3354 is

approximately 1.85 12. Scale parameter for γ=90 using (9), b=3.375L10 13. Probability of failure before 10000 hours according to (10):

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛−−=

85.1

10375.3exp1)(

LttR = 0.066

If we monitored the belt tension and keep it at 400N, the variation of the Pb would be 0. Substitute 0 in the above calculation will give us probability of failure:

P(T) = 0.054 This example shows that stabilisation of only one parameter can increase the reliability of the system by more than 20%. Note that the load levels are remain the same for both cases. We also can name such parameters, related to the bearing reliability as vibration, eccentricity of the air gap, rotor imbalance. All these parameters can be assessed and stabilised. Winding reliability can be approached in similar way.

6. Conclusion The problem of low reliability in most cases isn’t determined by rated life expectancy of the bearings, but by the lack of knowledge about the bearing operating conditions. Analysis of the rolling bearings’ reliability in induction motors shows that approximately half of the life expectancy variation is determined by the operational conditions. This makes benchmarking and condition monitoring the only possible tools to keep the bearings reliability high. The analysis of the target tree for particular application can help in finding the most important parameters to monitor to prevent unexpected failures.

6. Conclusion

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Split Mode Balancing for Generator Rotors

by Simon Hurricks

Background Information.

For a two pole generator rotor with a service speed of 3000 RPM it is not uncommon for them to be running above or close to the second critical speed.

During manufacture these rotors are balanced at full operating speed in a balance pit which allows full access to the body weight positions. The general procedure is to balance each of the relevant flexural modes separately i.e.

1st mode is balanced in the centre of the rotor, 2nd mode is balanced near the ends of the rotor but out of phase etc.

In this way the weights fitted do not affect the previously balanced mode.

When a generator has work done on it which may affect the rotor balance, such as removal and replacement of the end winding retaining rings there are three possible scenarios for re-balancing the rotor, namely:-

1. Full speed balancing, followed by over speed settlement of the end rings in a specialised balance pit.

2. Low speed balancing in a balancing machine.

3. Balancing the rotor insitu during re-commissioning.

The decision as to which method is to be employed depends on the amount of work which has been done on the rotor and therefore the degree of possible change to the rotor balance state.

For obvious reasons the full speed balancing option

is the preferred option as it not only allows the rotor to be balanced for all of the modes but also allows the rotor electrical properties to be tested under operational conditions. The main disadvantage is that there are no facilities in NZ the nearest being in Geelong near Melbourne. There is also a time penalty in getting the rotor to and from this facility.

Option 2 is a compromise which allows the rotor to be balanced as a rigid rotor but allows weights to be added in the body of the rotor. If done correctly, that is static balance corrected in the centre of the rotor and the couple balanced at the ends of the rotor, and then if the balance changes are minimal, there is a high probability that a reasonable balance can be attained. If required, a final trim can be done insitu. However the low speed balance does not allow the electrical properties to be tested under running conditions.

Option 3 is acceptable if there is a minimum risk of significant static imbalance change from the rotor work done.

The risk for option 3 is that if the static component, and therefore the vibration at the 1st critical speed, is too high then it may not be possible to balance this out using the end weight planes on the rotor.

Split mode Balancing Procedure.

For a rotor running above or close to the 2nd critical speed (GE102 runs above 2nd critical) and it is desirable to reduce the vibration from imbalance then it is likely that a standard two plane influence coefficient balancing technique will fail for the following reasons.

Depending on the work done on the rotor the most likely scenario is for the rotor to have a small amount

continued


of vibration from static imbalance and a larger degree of vibration from the couple imbalance. The reason being is that for a rotor running close to the 2nd critical there is amplification from the 2nd mode which is driven by the couple imbalance.

In addition to this a standard two plane balance program will give a solution to correct the small

amount of static imbalance but because of the insensitivity at the end planes will ask for an inordinately large weight change.

The solution to this problem is to vectorially separate the static component and the couple components and only add weights to correct the couple.

The vector diagram for this is below.

Vector Split diagram.

In the above diagram OA is the 1x vibration on bearing 1, OB is the 1x vibration vector on bearing 2. S is the midpoint of AB. Then OS is the static component, and SA and SB are the couple components on bearing 1 and 2 respectively. If we add equal weight at each ends of the rotor which are 180° out of phase the static component is unaffected and we can balance out the couple. We can now use a simple single plane balance procedure using the couple component at one end in the calculation and for each weight change we simply add the same weight at the opposite end but 180° out of phase. We thus correct the couple with no effect on the static component.

Proximityprobes. A proximity probe has two components:-

1. The driver/demodulator. 2. The probe itself.

In the probe tip is a fine coil of wire around a ferrite core. The driver injects a 10MHz RF into the probe coil which acts as an aerial and the RF signal radiates from the probe tip.

The RF signal induces eddy currents in the shaft surface. This in turn reduces the amplitude of the RF signal and the driver /demodulator outputs a DC voltage proportional to the RF signal amplitude. This is the DC gap value from the probe and is directly proportional to the gap between the shaft and the probe.

270° 90°

Rotation

4 6 5

180°

O

A

B

S

0°

continued

In the above diagram OA is the 1x vibration on bearing 1, OB is the 1x vibration vector on bearing 2. S is the midpoint of AB. Then OS is the static component, and SA and SB are the couple components on bearing 1 and 2 respectively.

If we add equal weight at each ends of the rotor which are 180° out of phase the static component is unaffected and we can balance out the couple.

We can now use a simple single plane balance procedure using the couple component at one end in

the calculation and for each weight change we simply add the same weight at the opposite end but 180° out of phase. We thus correct the couple with no effect on the static component.

Proximity probes.

A proximity probe has two components:-

1. The driver/demodulator.

2. The probe itself.




If the shaft is vibrating relative to the probe then the DC gap value varies about a mean value. The mean value is the average DC gap and the variation becomes an AC voltage proportional to the vibration.

Bently Nevada 8 mm prox probes have a calibration constant of 7.87 mv/µm (200 mv/mil (thou)).

Run Out.

Below is a definition from Bently Nevada.

Mechanical Run-out is a measure of the shaft’s deviation from a perfectly uniform radius as its circumference is traversed. This type of run-out can be measured by a dial indicator.

Electrical Run-out is a measure of a shaft’s electrical property variations as its circumference is traversed. This type of run-out cannot be measured by a dial indicator.

Because a proximity probe senses both types of run-out, it is customary to speak of Total Indicated Run-out (TIR) which is simply the sum of mechanical run-out and electrical run-out. In most cases, when run-out is discussed in conjunction with proximity probes, it is understood to mean TIR.

Electrical run out can be the result of magnetic fields in the shaft surface (removed by degaussing) or changes in the material permeability, a classic case of which is metal spray repairs.

Balancing Using Proximity Probes.

The run out on a shaft as defined above is sensed by the proximity probe as soon as the shaft begins to rotate as it is present at all speeds.

The object of balancing is to remove any dynamic forces on the bearings produced by imbalance masses in the rotor system. The effect of these forces is measured by the amplitude and phase of the 1X vibration.

If we have a perfectly balanced shaft the proximity probe will still read the run out value at the measurement location. If we use this value to balance the shaft we are in effect unbalancing it.

To get around this problem the 1X run out vector is measured at a speed such that there are no dynamic forces from the imbalance and this vector is subtracted from all subsequent measurements. As a guide line 10% of running speed is often used as the slow roll compensation speed.

This can be done in the instrument or allowed for in the balance software.

When perfectly balanced the 1X RPM vibration as read by the proximity probes will be equal to the run out values.

Vector Split diagram.

In the above diagram OA is the 1x vibration on bearing 1, OB is the 1x vibration vector on bearing 2. S is the midpoint of AB. Then OS is the static component, and SA and SB are the couple components on bearing 1 and 2 respectively. If we add equal weight at each ends of the rotor which are 180° out of phase the static component is unaffected and we can balance out the couple. We can now use a simple single plane balance procedure using the couple component at one end in the calculation and for each weight change we simply add the same weight at the opposite end but 180° out of phase. We thus correct the couple with no effect on the static component.

Proximityprobes. A proximity probe has two components:-

1. The driver/demodulator. 2. The probe itself.



270° 90°

Rotation

4 6 5

180°

O

A

B

S

0°


TEST YOUR KNOWLEDGE - PART 43 OF A SERIES

continued

1 Which of the following is true about spherical roller bearings?a They always produce lower levels of high-frequency vibration than deep-groove ball bearingsb They are most commonly monitored using eddy-current probesc They cannot be used for shaft speeds above 2000 rpmd Sometimes data collected in the axial plane can yield good information on their condition.

2 The name-plate of a German-made generator had the word “Lager” on it. This word means ……

a Bearing

b Grease

c Beer

d Country of manufacture

3 On a 2015 Holden VF Calais V Sportwagon the vehicle speed or other vehicle parameters can be displayed on the windscreen. One display option is a parameter that indicates how aggressively the car is driven around corners. Which of the following do you think is being displayed in this instance?

a Displacementb Velocityc Accelerationd B or C depending on the speed of the vehicle at the time

4 What should you be mindful of when balancing fans that have hollow aerofoil-shaped blades on the impeller?

a The blades will have a strong natural frequency of 68 Hzb The fan will be noisier in operation than a paddle-wheel typec If there are any holes in the blades, water, rust or product can enter the blade and

cause unbalance d It will be necessary to run the fan in reverse when conducting in-situ balancing

5 Which of the following is true about line-current analysis of 3-phase induction motors? a No meaningful data will be obtained unless all 3 phases are checkedb The spectrum collection parameters should be set to minimal resolution to speed up data

collectionc The motor must be run unloaded during the testsd It is advisable to have the assistance of a qualified electrician when undertaking the tests


ANSWERS on page 36

Further enquiries can be directed to: Carl Townsend at Carlton Technology Ltd

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P O Box 18046 Merrilands,New Plymouth 4360, NZ

email: [email protected]

6 Some electric motors running with journal bearings have a groove machined in the shaft near the DE housing which has a pointer on it. These things serve which purpose?

a They indicate that the motor is a high-voltage oneb They provide guidance as to where the rotor is running axially relative to the magnetic centrec The groove in the shaft is a stress-raising point so that the shaft fails cleanly if the torque limit is

exceededd These items indicate that the motor was manufactured prior to 1972

7 When carrying out an insitu balancing operation, the phase changes 180 degrees after the addition of your trial weight. What might this indicate?

a The trial weight is too heavyb The trial weight is too lightc There is looseness in the assemblyd The trial weight is 90 degrees away from the correct position to balance the rotor

8 When configuring a measurement in a database set-up, you are offered the option of ticking a box for the supply of ICP Power. You would tick this box if what type of transducer is going to be used?

a Eddy-current probeb Tachometerc Moving coil velocity transducerd Accelerometer (not a charge-mode type)

9 If you do not have a test-rig in your NZ office, how can you sometimes test that your tachometer is functioning?

a By pointing it at your computer screen and obtaining a speed reading of 60 cpmb By pointing it at a fluorescent light and obtaining a speed reading of 6000 cpmc By pointing it at a fluorescent light and obtaining a speed reading of 10000 cpmd Any of the above could be correct

10 What is true about the instrumented hammers used in modal analysis?a They must weigh at least 500 grams to be effectiveb Interchangeable heads of varying hardness are often usedc The impacting should only be done in the horizontal directiond Both a and c are correct


QUIZ

ANSWERS

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Spectrum 79 February 2016

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