PdM/RxM€¦ · systems design specialist at Penn State (). Now, about 300–350 buildings are...

TECHNOLOGY REPORT

PdM/RxM

TABLE OF CONTENTSThe Road to RxM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

How industry leaders in energy, oil and gas, and more are mapping out a smarter

future for maintenance.

Business Analytics and Reliability Centered Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . 11

Business Analytics enable your management teams to take decisive action

and predict the future

Addressing Challenges of Online Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Managers need a predictive maintenance strategy that integrates with existing enterprise

infrastructure and automates the collection of data

Something in the Air: Ultrasound for Compressed Air Leak Detection . . . . . . . . . . . . . . .22

Here’s how to use airborne ultrasound to identify leaks and reap big savings

AD INDEXGTI Predictive Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

National Instruments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

PRUFTECHNIK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

UE Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

www.plantservices.com

TECHNOLOGY REPORT: PdM/RxM 2



The Road to RxMHow industry leaders in energy, oil and gas, and more are mapping out a smarter future for maintenance.

by Sheila Kennedy, CMRP, contributing editor

Who would have imagined how dramatically the industrial internet of things

(IIoT) would elevate reliability and maintenance practices? Today, we have

sophisticated sensors monitoring multiple variables, closing information gaps,

eliminating data silos, and populating Big Data repositories in the cloud, where artificial

intelligence (AI), advanced pattern recognition (APR), machine learning (ML), and advanced

analytics work their magic on common industrial challenges.

Predictive maintenance (PdM) gave us our first taste of the power of monitoring individual

machine conditions. With prescriptive maintenance (RxM), data is assimilated from diverse

process and performance variables and woven into actionable recommendations (or “pre-

scriptions”) on what to do, when to do it, and how.

The benefits are readily evident – better-quality data, earlier problem detection, more timely

and accurate response, and perhaps of the most importance, less reliance on manual knowl-

edge capture. Following are some companies that are on the cusp of this new level of main-

tenance maturity called RxM.

NETWORK PREPARATION AT PENN STATE Maintenance strategies such as PdM and RxM are possible only in connected environ-

ments. Tempered Networks recently helped Penn State’s Office of Physical Plant (OPP)



instantaneously connect, segment, secure,

and manage all of its network devices

cohesively despite unique building and

campus challenges. As a result, OPP is

now making real-time control adjustments

based on conditions, entering the predic-

tive stage of maintenance and preparing

for a future in which recommendations will

be prescribed

Previously, each building was a separate

entity. A lot of the systems in use were

standalone, and there was a server for ev-

ery application. “It causes headaches for

maintenance when buildings are disjointed

like that,” says Tom Walker, environmental

systems design specialist at Penn State

(www.psu.edu).

Now, about 300–350 buildings are connect-

ed at University Park, with all or most serv-

ers housed at the data center. Everything is

on a virtualized server; hardware is shared

among multiple systems; and authorized

personnel have instant access to the sys-

tems. “This increased our resiliency, reliabil-

ity, and overall uptime,” Walker says. “It also

gave us the path to start sharing data with

other systems and stakeholders.”

For instance, OPP is now working to en-

able fault detection and diagnostics within

the building automation systems, which is

expected to help reduce energy use and

maintain optimum facility operation. OPP’s

new energy dashboard visualizes when an

energy problem emerges in a building so

the issue can be addressed proactively. In

the future, OPP would like it to prescribe

what to do based on ML and data analytics

from the connected systems.

Efforts are also underway to automate work

orders in IBM’s Maximo based on certain

fault conditions and eventually prescribe

corrective actions. “Right now the work

orders are only telling that there’s an is-

sue that needs to be investigated,” Walker

explains. “We’re working with our Maximo

group on being able to feed more data on

the assets.”

Walker’s biggest lesson learned so far is

that the use of analytics packages that

read directly from the server is a better

option than pulling data directly from the

controllers, which does not scale. There

are also issues with legacy control sys-

tems. “With Tempered Networks, we’re

putting a shell around all of our legacy

systems by locking them out and using

microsegmentation to say only this device

can talk to this server,” says Walker. “It’s

really solved a lot of problems.”

Segmentation and isolation has become a

best practice, but it is fragile using tra-

ditional technologies. “You can set it up

once, but as time goes on, it becomes

impossible to maintain, so it’s important

to keep it simple,” observes Erik Giesa,

vice president of products at Tempered



Networks (www.temperednetworks.com).

Instead of using a traditional enterprise

IT solution to force-fit connections, Tem-

pered Networks technology was borne

in an ICS and OT data environment and

bridges legacy systems in a simplified

manner, Giesa says.

PRESCRIPTIVE SERVICES FOR REFINING NZ Industry has come to expect maintenance

service providers to employ state-of-

the-art technologies and practices. The

outcome-based maintenance service for

industrial control systems from Honeywell

Process Solutions is relied upon by com-

panies such as Refining NZ, New Zealand’s

only oil refinery.

Peter Smit, head of process control at Re-

fining NZ (www.refiningnz.com), says: “The

Honeywell Assurance 360 program we have

in place provides us with the confidence

that we have our Honeywell distributed

control systems and Honeywell Advanced

Solution applications at an agreed level of

availability. We are very clear what out-

comes we expect, and this allows Honey-

well to leverage their knowledge and re-

sources to meet the agreed outcomes in a

structured and planned way.”

Steve Linton, director of programs and

contracts at Honeywell Process Solutions

(www.honeywellprocess.com), explains the

underlying goal. “We are trying to facilitate

achievement of our customers’ business

drivers and provide the outcomes they ex-

pect,” he says, “whether it’s control system

performance, control system availability, or

reduced incidences on the control system.”

Tools such as planned, preventive, predic-

tive, prognostic, and prescriptive analyt-

ics and maintenance aid in driving toward

those outcomes. Prescriptive approaches

are being beta-tested at some customer

sites.

With RxM, Honeywell’s goal is to amalgam-

ate data across multiple control systems to

provide insights that say, “There is X prob-

ability in X time frame that X is going to

happen, so go look at these things to pre-

vent an undesirable outcome.” To do this,

information from multiple customer systems

is put into a data lake in the Honeywell Sen-

tience IoT platform, which is appropriately

controlled, cordoned off, and anonymized.

Self-learning algorithms use and analyze the

data and provide information that the cus-

tomer can use to better maintain its control

systems.

PRESCRIPTIVE RELIABILITY ANALYTICS FOR MOLCorrosion, fouling, opportunity crudes, and

resulting process fluctuations are the most

common operative challenges faced daily

at MOL, an integrated oil, gas, and petro-



chemicals company based in Hungary. It is

a member of MOL Group, one of the largest

companies in Central and Eastern Europe.

MOL Group’s 2030–Enter Tomorrow pro-

gram and recent strategic initiatives require

a dynamic enterprise-operations-focused

data and information infrastructure to

improve productivity and increase process

safety performance, says Gábor Bereznai,

maintenance engineering manager at MOL

(www.mol.hu/en). “Crude analysis, process

simulations, continuous data monitoring,

and early failure detection are the only pos-

sible answers to keeping our processes safe

and under control,” Bereznai says.

MOL began its journey to refinery mainte-

nance excellence with reliability-centered

maintenance (RCM) almost two decades

ago. At that time, a race to acquire software

led to implementation islands and a lack of

deliberate business process re-engineering.

In the next era, the focus was on software

integration and connecting the systems

with the corporate SAP ERP solution. MOL’s

daily operations have come to rely on the

company’s successful integration of asset

management software, including Emerson

AMS with SAP EAM and OSIsoft’s PI System

with SAP PM.

The PI System provides the real-time

operational data infrastructure and con-

figurable, streaming analytical platform

for MOL’s refining division. Predictive and

condition-based maintenance, data aggre-

gation, and health scoring is done in the PI

Asset Framework (PI AF) and sent to SAP

PM, which generates the work orders.

MOL is using a “layers of analytics ap-

proach,” with human analytics and real-

time/streaming analytics providing a

foundation for higher-level, operationally

focused ML/AI, explains Craig Harclerode,

global industry principal for O&G/Petro-

chem at OSIsoft (www.osisoft.com). MOL

built momentum and awareness of the

power of analytics by asking the opera-

tions managers what problems needed to

be solved and then quickly solving them.

“Once they had an analytical foundation,

they moved to identifying areas where

more-advanced prescriptive and predic-

tive analytics would have value and began

developing ML applications accordingly,”

Harclerode says, noting that MOL current-

ly has more than 25 ML-based applications

in production.

This approach works because, as Bereznai

explains, IT/OT transformation is a long

journey that involves not only architec-

tural and analytical method changes but

also multilevel synergies among people

and processes.

“This is a really long journey, especially in

terms of mindset change and cultural de-



velopment,” Bereznai says. “The technology

and software side is much easier to change

than the mindset, and the impact of this is

underestimated.”

The efforts are paying off. MOL’s digital and

downstream business transformation has

delivered $1 billion in its first four years, and

the goal for the next two-year period (2017-

2018) is an additional $500M in EBITDA.

PRESCRIPTIVE PERFORMANCE ANALYTICS FOR TATA POWERSoftware companies such as AVEVA are

working quickly to answer the call for RxM.

“We are building prescriptive maintenance

and analytic capabilities into all of our asset

performance management solutions to help

our customers optimize the entire asset life-

cycle and to ensure they have access to the

most advanced technology available,” says

Sean Gregerson, global director of asset

performance management sales at AVEVA

(www.aveva.com).

Tata Power (www.tatapower.com), one of

the largest integrated power companies

in India, has rolled out AVEVA’s Predictive

Asset Analytics software to 10 units at three

plants to enhance the reliability of its crit-

ical-power plant equipment. The rollout is

putting Tata Power in a position to quickly

incorporate RxM capabilities.

The utility set its sights on remote, fleet-

wide continuous monitoring and diag-

One recent catch

by Tata Power

yielded an

estimated

$270,000

in cost savings.

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nostics of critical asset health and perfor-

mance in 2014 with the goal of improving

efficiency, enabling proactive mainte-

nance, and avoiding unplanned down-

time. It built a new Advanced center for

Diagnostics and Reliability Enhancement

(ADoRE) powered by Predictive Asset

Analytics.

The software learns an asset’s unique oper-

ating profile during all loading, ambient, and

operational process conditions. When exist-

ing machinery sensor data is compared with

real-time operating data, subtle deviations

are revealed. Alerts and fault diagnostics

are generated and plant personnel are dis-

patched quickly to take corrective action.

One recent catch yielded an estimated

$270,000 (U.S.) in cost savings. Analytics

revealed that the top thrust and guide bear-

ing temperatures of some circulation water

pumps were exceeding expected levels.

During a brief planned outage, clogging in

the bearing-cooling water line was identi-

fied and cleared, thus normalizing subse-

quent operation.

“Tata Power demonstrates the power of

using analytics to move away from a reac-

tive maintenance strategy,” AVEVA’s Gre-

gerson says. “By catching problems early

using APR and ML, the company was able

to reduce maintenance costs, minimize

unscheduled downtime, and prevent equip-

ment failures.”

PRESCRIPTIVE SCHEDULING FOR DEVON ENERGY Prescriptive approaches can be simple to

introduce incrementally. Devon Energy

(www.devonenergy.com) has thousands

of batteries of tanks that collect water

and oil during the course of operations,

and how that liquid is scheduled for haul-

off has recently become prescriptive.

Real-time data engineer Don Morrison

described the transition in a presentation

at the ARC Industry Forum in Orlando in

February.

Previously, scheduling liquid tank haul-offs

for the Oklahoma City-based independent

oil and gas company involved collect-

ing data from multiple parties in an Excel

spreadsheet and then using that file to

create schedules. A centralized, more-ac-

curate, on-demand process was needed to

prescribe when, where, and how haul-offs

would be needed.

Morrison explained: “We already had SCA-

DA systems monitoring oil and water tank

levels, so why not use them to detect when

haul-off trucks are on site and how many;

whether water or oil is removed from the

tanks and how much – we only want full

loads – and the fill rate?”

Two specific answers were sought: Could

the engineers predict when the next load

needed to occur so they could schedule

the right number of trucks 3–4 days out?



How Devon Energy’s tank liquid is scheduled

for haul-off has become prescriptive.

Could they gain enough data to “grade”

their service providers?

Devon Energy chose Seeq analytics soft-

ware to quickly detect haul-off events

based on real-time OSIsoft PI data. With the

push of a “get loads” button, all of the data

from PI are pulled; forecasts up to three

days out are generated; and the spread-

sheet gets filled automatically. The results

are reported in Microsoft Power BI, where

they can be sliced and diced as needed.

Excel was retained in the first stage be-

cause “we didn’t want to change every-

thing the users were doing and they were

comfortable using it,” Morrison explained.

Other future goals for Devon Energy in-

clude auditing and grading haul-off vendor

performance and potentially incorporating

opportunities such as RxM, smart contracts,

and blockchain.

As more companies like these advance to

prescriptive analytics and RxM, prescriptive

maintenance has the potential to further

heighten visibility and respect for the main-

tenance profession and its positive impact

on the bottom line.

Sheila Kennedy, CMRP, is managing director of Additive

Communications. Contact her at [email protected].

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Business Analytics and Reliability Centered MaintenanceBusiness Analytics enable your management teams to take decisive action and predict the future

By Paul Berberian, GTI Predictive Technology

BUSINESS ANALYTICS“Per Gartner, companies who do not adopt a Business Intelligence strategy in the next five

years will be at a competitive disadvantage in the marketplace,” Sean Ingalls, Customer Ac-

count Manager, The Resource Group.

Recent trends show that more and more manufacturers are looking to expand in the United

States or move capacity back from overseas. To do this, companies will have to look for

new, smarter ways to improve performance, increase machine reliability, maximize work-

force effectiveness, and increase uptime.

Business Analytics (BA) will be a key factor in reaching these goals. Informed decisions

must be made at every level – production, maintenance, purchasing, engineering, and IT –

enabling the management team to take decisive action and predict the future.

Strategies for business analytics are very similar to maintenance strategies. One approach is

the “what happened” strategy. The business plan is, “let’s look at the data we have and find

out what bad thing just happened and why.” This is very similar to a run-to-failure strategy

in maintenance. Something went wrong; now let’s try to find out why. Here we are focusing

on making business and maintenance decisions using historical data.



Another strategy focuses on performance

to plan. This is a decision-making or perfor-

mance management strategy where deci-

sions become more real-time, in the mo-

ment. This business strategy compares to

using preventative maintenance strategies.

The final strategy we will consider is pre-

dictive insight. This is when we start to

ask what will happen next and how can we

influence a positive outcome. We can now

use business analytics as predictive tools

to anticipate future events and avoid them

or take advantage of them. This is when

a maintenance program can start moving

toward Reliability Centered Maintenance

(RCM).

So, why is it important to compare busi-

ness analytic strategies to maintenance

strategies? Because, in the end, they are

all connected. Sales and marketing are

using business analytics to predict market

trends and opportunities. Production has

to be prepared to adapt to these changes.

Purchasing has to have materials and

parts in the pipeline to support the pro-

duction schedule. And, maintenance has

to provide the uptime and capacity. All

of these different groups within the or-

ganization have to work together to take

advantage of opportunities in the market

and create a new future.

ENTERPRISE ASSET MANAGEMENTTo accomplish these goals, many compa-

nies are using Enterprise Asset Management

(EAM) systems. An EAM solution manages

the entire optimal life of physical assets to

maximize value. Enterprise refers to the entire

operation of a company and the manage-

ment of assets across departments, locations,

facilities, and even business units. The goal of

an EAM solution is to improve utilization and

performance, reduce costs, extend asset life

and improve the return of assets (ROA).

An effective implementation of an EAM will

include whole life planning, life cycle cost,

and planned maintenance, and will lead to

industry best practices. Companies can now

see the impact and relationships between

operations, engineering, maintenance, per-

sonnel, and life cycle costs.

“Strategies for business analytics are very similar to maintenance strategies: something went wrong; now let’s try to find out why.”



Without high-quality and consistent data,

delivered on an established schedule, the

ROI on the EAM investment is lessened. The

data provided must be provided automati-

cally from SCADA systems, a PLC network,

Distributed Control System (DCS), on-line

Condition Monitoring networks, and/or other

types of control systems that can consis-

tently feed machine health data to an EAM

or CMMS. Real-time or near real-time data is

essential to monitoring of machine health.

DATA FOR ANALYTICS“More simply, the more you raise the quality

of the data your organization interacts with,

the higher the statistical probability your

organization will make a better decision,”

R. Taggs, P. Sage, M. Osana, D. Tepora, J.

Mark de Asis, TEAM Global: The Maximo

Manager’s Guide to Business Performance

Management

Step one is to set goals for the machin-

ery health monitoring program and to

determine what machine condition data

needs to be in the maintenance software

for optimal decision making. The data

required can vary depending on the type

of machine. Pressure, flow, temperature,

ultrasound, oil analysis, and vibration data

and/or a combination of all of these or

other inputs can provide the data required

to assess machine health.

Step two is to figure out how the data will

be collected and how often. Depending

on the criticality of the machine to your

production you might want to collect data

more often. High-speed, critical machines

(i.e., steam or gas turbines) with fast

failures modes may need to be monitored

continuously.

On-line data provides the most consistent

and ubiquitous data as it can automati-

cally be fed to the EAM or CMMS. Manually

collected data is, by nature, collected less

often and the task of manually moving the

data to an EAM or CMMS is time-consuming

and expensive.

There is significant value to good data in

the reliability maintenance software. Dave

Bertolini of People and Processes, Inc.

writes, “Perhaps it will surprise you to learn

that 90% of Computerized Maintenance

Management Systems contain little data

worth trying to utilize for sound mainte-

nance management decision making.” It’s

not bad software – it’s incomplete or irrel-

evant data:

• Good data increases production uptime

• Good data protects against environmen-

tal issues and regulatory fines

• Good data allows the reliability mainte-

nance team to work more efficiently

• Good data reduces spare parts inven-

tory while ensuring that the parts that

are required are on-hand

High-quality and consistent asset condition

data is needed to transition from a reactive



to a predictive maintenance culture effec-

tively. To date, the asset management revo-

lution is focused on high-end assets. Online

integrated condition monitoring solutions

have traditionally been too expensive to

deploy on hundreds of assets and could not

be justified.

INDUSTRIAL INTERNET OF THINGS (IIOT)In order to obtain the “Big Data” required

for analytics, the data must be available,

collected and presented in an organized

manner. The Industrial Internet of Things is

becoming the conduit for ubiquitous data.

With both predictive maintenance solu-

tions and IIoT continuing to grow in popu-

larity, it’s important to reflect on their

utilization in 2018 to identify future trends

and usage rates as we approach 2019. Af-

ter reviewing industry survey results and

analyzing the scale of IIoT development, it

is clear that both manufacturing and ma-

chine maintenance will become more and

more automated.

When you consider that almost 60% of all

manufacturing tasks can be automated,

it’s no surprise that IIoT is expected to

continue impacting a variety of indus-

tries. More than just an Industry 4.0 buzz-

word, global management consulting

and professional services firm Accenture

estimates that IIoT could contribute an

astounding $14.2 trillion to the global

economy by 2030. Despite future projec-

tions and a high percentage of executives

recognizing their potential in the predic-

tive maintenance landscape, there are

some aspects of IIoT that are less likely to

be adopted, like robotic-assisted repairs.

As IIoT and predictive maintenance tech-

nologies grow more advanced and acces-

sible, many manufacturers have identified

several barriers to complete adoption.

Understanding how the two technologies

interact remains a top concern. Currently,

50% of manufacturing professionals report

that their plant staff fails to recognize the

potential that IIoT predictive maintenance

has, further exemplifying this knowledge

“High-quality and consistent asset condition data is needed to transition from a reactive

to a predictive maintenance culture effectively.”



gap. Manufacturers have also identified a

skill shortage of data scientists as an addi-

tional inhibitor to harnessing the power of

machine analytics.

The costs of the technical tools (smart-

phones, laptops, tablet computers) are

coming down, and those economies of

scale are allowing providers to offer af-

fordable solutions for condition monitor-

ing. Modern networks allow the owner to

outsource data analysis in real-time and

“expert” software programs have im-

proved over the years.

Wireless technology and advanced net-

works that are being adopted as industry

standards in other business models (prob-

ably within your company) are being ap-

plied as reliability maintenance solutions,

eliminating the old expensive, hardware

intensive solutions of the past. New systems

are now scalable to enterprise and deliver

ubiquitous data.

Today’s CBM software allows machine

faults to be readily identified and priori-

tized, from machines that do not currently

have problems. Trends can be built faster

with on-line monitoring than route-based

programs. These new solutions are also

scalable – from one machine to an enter-

prise level – on a single platform.

The combination of a well implemented

reliability maintenance software package

and high-quality; consistent data can help

any maintenance program achieve world-

class Reliability Centered Maintenance

status.

Are you and others in your organization

getting the data you need to make good

decisions? Start today to find ways to add

high-quality, consistent machine health

data to your reliability maintenance soft-

ware program.

Paul Berberian is a Condition-Based Monitoring Spe-

cialist for GTI Predictive Technology (www.gtipredic-

tive.com). He has more than 12 years of experience in

the maintenance reliability industry. He is a frequent

contributor to Plant Services Magazine and other

industry publications.

©2019 National Instruments. All rights reserved. LabVIEW, National Instruments, NI, ni.com, are trademarks of National Instruments. Other product and company names listed are trademarks or trade names of their respective companies. NI is an Affiliate member of the Intel® IoT Solutions Alliance. 33908

BETTER UPTIME STARTS WITH

BETTER DATA

Intel® and NI have partnered together to help companies act on their machine health data to improve the availability of their assets. NI’s platform enables maintenance specialists to collect multiple types of measurements with one tool, automate alarms, and perform analysis of real-time and historical data. Intel® technology enables real-time analysis and improves network bandwidth efficiency.

See how better data can improve your maintenance program at ni.com/mcm.

http://ni.com/mcm



INTRODUCTIONAccording to the Electric Power Research Institute (EPRI), online monitoring is the imple-

mentation of applications for monitoring, maintaining, and optimizing assets from a central-

ized location.

More than ever, organizations need a dependable maintenance program that helps allevi-

ate risks and can lead to millions of dollars in return on investment. Reliability engineers and

maintenance professionals are keenly aware of the optimal balance of plant safety, reliabil-

ity, and financial returns.

They know they must deploy maintenance strategies that address these three objectives:

• Increase revenue through the maximum uptime and optimal efficiency of machinery. With

properly functioning assets, organizations can achieve maximal output within the con-

straints of the facility.

• Reduce costs by minimizing downtime and scheduling maintenance only when neces-

sary. Being able to identify developing issues with enough lead time to properly schedule

maintenance during planned downtimes allows maintenance managers to optimize the

workforce and increase the mean time between failures.

• Reduce risk and increase safety through decreased worker contact with large, dangerous

machines in potential hazardous environments. In addition, properly functioning machines

Addressing Challenges of Online MonitoringManagers need a predictive maintenance strategy that integrates with existing enterprise infrastructure and automates the collection of data

By Matthew Bollom, National Instruments



can remove uncertainty in business opera-

tions, which prevents catastrophic failure

and unforeseen outages.

This article shows how online condition mon-

itoring can help organizations achieve better

insight into the health of their assets. They

can use this data to drive predictive mainte-

nance programs, which allows maintenance

managers to schedule and plan maintenance

only when necessary. This leads to more rev-

enue, reduced cost, and advance warning of

potential failures while increasing safety.

FROM MANUAL TO REMOTE MONITORINGAs assets grow more important to the

performance of a facility, maintenance

managers use technicians to collect as-

set condition data through manual, route-

based measurements. This data provides

the context necessary to better understand

asset health and allow organizations to

schedule maintenance when necessary.

However, as the number of assets that

demand this attention grows in the facility,

these technicians are spending upwards

of 80 percent of their time collecting data

and 20 percent analyzing it to determine

the root cause of issues.

Further studies by the International Data

Corporation (IDC) show that 22 percent

of data stored digitally is documented

well enough to be analyzed and that only

5 percent of data is actually analyzed. In

addition, organizations are finding it more

challenging to locate, hire, and train new

equipment specialists while today’s experts

are retiring at a rapid pace.

As maintenance managers build a mainte-

nance strategy, they report difficulty in find-

ing enough experienced equipment special-

ists, spending too much time collecting data

versus analyzing it, feeling discouraged

with inconsistent diagnostics and a lack of

insight into overall reliability, and working

with new technology that is more complex,

expensive, and difficult to maintain.

Plants and enterprises are constrained

because monitoring systems are tied to

equipment providers, so the trend is to seek

“Plants and enterprises are constrained because monitoring systems are tied to equipment providers, so the trend is to seek platform

solutions that are independent of equipment providers, thereby gaining flexibility.”



platform solutions that are independent of

equipment providers, thereby gaining the

flexibility to have one system that can moni-

tor equipment from any supplier and then

integrate this for accurate diagnosis.

Today, packages custom-built for the plant

feature monitoring functions but lack flex-

ible processing capabilities or I/O count. Or,

on the other hand, they offer I/O count but

limited programming options to customize

the system behavior.

As utilities and enterprises move toward

centralized monitoring, the integration of

advanced monitoring applications with

existing monitoring efforts enables a plant

view of operations and maintenance along

with the back-end integration into the

enterprise. This can help reliability and

maintenance managers achieve the opti-

mal balance of safety, reliability, and cost

returns.

Managers need a predictive maintenance

strategy that integrates with existing

enterprise infrastructure and automates

the collection of data on more assets to

predict asset failure in advance of cata-

strophic and costly repairs. This strategy

involves data acquisition and analysis

systems that continuously acquire and

compare key measurement indicators,

such as vibration and power consumption,

to baseline normal behavior to pinpoint

any equipment health degradation. When

the systems detect this, they immediately

alert operations staff to examine the issue.

These condition indicators can help influ-

ence decisions about when to perform

maintenance, which can lead to more rev-

enue, reduced costs, and advance warning

of impending risks of failure while increas-

ing safety.

CONSIDERATIONS & BENEFITS OF ONLINE MONITORINGBefore choosing a condition monitoring

system, maintenance managers need to

understand which assets and which failure

modes should be monitored. They must

make decisions based on the breadth and

number of assets and the types of measure-

ments needed to detect the failures.

Once the assets and necessary measure-

ments have been identified, maintenance

managers should consider the following

when choosing a vendor for a condition

monitoring solution:

• The ability of the solution to scale with

evolving needs, such as support for new

types of algorithms, a wide variety of I/O

and emerging sensors, and expansion to

large numbers of systems.

• An openness that allows for access to the

raw engineering measurements so new

and innovative analysis techniques can be

adapted and the solution can be extended

to meet the maintenance program re-

quirements.

• Interoperability with third-party hard-

ware and software packages so the solu-



tion can integrate with existing CMMS

and ERP systems and any data historians

or process management enterprise soft-

ware used.

• Rugged mechanicals and a breadth of

available analysis algorithms.

• A monitoring hardware and software solu-

tion for a price that allows for the

• solution to scale to a larger percentage of

fleet assets.

• The services to help facilitate the end-to-

end solution from asset to IT infrastruc-

ture, either directly or through a network

of partners.

Though the financial benefits are attrac-

tive, additional industry trends such as

lower cost sensors, automated monitoring

systems, and the emergence of intelligent

analytics are also fueling the adoption of

automated solutions for online monitoring.

When compared with other maintenance

approaches, online monitoring and diag-

nostics for predictive maintenance offer the

following benefits:

• Workforce optimization . Online condi-

tion monitoring helps ensure that the

limited specialized personnel are spend-

ing maximal time on the highest value

tasks such as assessing required mainte-

nance rather than low-value tasks such

as traveling to assets, setting up tests,

and recording data.

• Fewer gaps in data . Online condition

monitoring ensures data accuracy and

provides continuous data collection.

Manual measurements offer only a few

snapshots of manually recorded data

for any given asset every month, if any

at all, which increases the possibility of

data errors or missed events.

• Improved diagnostics . By using a single

database with online condition monitor-

ing, more historical trend and baseline

data is available for predicting faults

with greater statistical significance. This

ensures consistent analysis and elimi-

nates reliance on the experience and

knowledge of an equipment specialist.

These online condition monitoring sys-

tems provide the greatest insight into

overall reliability, which helps companies

thoroughly understand their operations

and make business decisions.

Matthew Bollom is offering manager for maintenance

& monitoring applications at National Instruments

(www.ni.com). He is responsible for understanding

customer needs and owning the entire experience

from discovery to deployment of the solution for

industries such as power generation, oil & gas, and

mining & metals. Matthew has experience with a

variety of topics including data acquisition, wireless

communication, and data analytics as part of condi-

tion monitoring and IIoT applications.

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Contrary to what some might think, compressed air is not free. In fact, for the en-

ergy it takes to produce it to what is generated as a result, it is often considered

the most expensive utility in a typical manufacturing facility. To add to the prob-

lem, the U.S. DOE notes that more than 50% of all compressed air systems have energy-

efficiency problems. Air compressor experts have also estimated that as much as 30% of

compressed air generated is lost via leaks in the compressed air system.

Often, when a compressed air system struggles to meet current demands on the system,

spare compressors are rented and used as backups or an additional compressor is installed.

Both strategies are expensive, and depending on the size of the compressors needed, they

could equate to hundreds of thousands of dollars.

Because compressed air systems inherently have leaks, regardless of piping, use, and de-

sign, implementing a compressed-air leak-management program can be an economical

and effective way to improve the efficiency of any compressed air system. Having a com-

pressed-air leak-management program in place that is designed to identify and repair com-

pressed air leaks before they become a large problem can save time, money, and energy.

Proper planning and creating a sense of awareness by educating employees on how costly

compressed air leaks can be is integral to achieving success with any compressed-air leak-

management program.

Something in the Air: Ultrasound for Compressed Air Leak DetectionHere’s how to use airborne ultrasound to identify leaks and reap big savings

by Adrian Messer, CMRP, UE Systems



Compressed air and compressed gas leak

detection remains the most widely used

application for airborne ultrasound technol-

ogy. Employing ultrasound to locate com-

pressed air and gas leaks and then making

the necessary repairs can have tremendous

payback. Recent advancements in com-

pressed-air leak detection and reporting

allow organizations to quantify dollars lost

and the CFM loss associated with com-

pressed air leaks. An effective ultrasonic

compressed-air leak survey will focus on

seven key factors: evaluation, detection,

identification, tracking, repair, verification,

and re-evaluation. By implementing these

steps, a typical manufacturing plant could

reduce its energy waste by roughly 10% to

20%. As an example, a 1/8” leak at 100 psi

of compressed air at 22 cents per kilowatt

hour has an annual cost of $2,981.

AIRBORNE ULTRASOUND: HOW DOES IT WORK?There are three generic forms of ultra-

sound technology: pulse/echo, power, and

airborne/structure-borne. Pulse/echo is

the most recognized form of ultrasound,

as this is the medical form of ultrasound.

With power ultrasound, as in an ultrasonic

cleaner, high-frequency sound waves are

emitted. These high-frequency sound waves

have energy, and they clean parts and

various materials. The form of ultrasound

technology that is used for compressed-air

leak detection is airborne ultrasound. Air-

borne ultrasound relies on high-frequency

sound waves that are above the range of

normal human hearing. Humans are able to

receive sound within a frequency range of

20 Hertz (Hz) to 20 kilohertz (kHz), with the

upper threshold of normal human hearing

between 16 kHz and 17 kHz. The ultrasonic

range begins at 20 kHz. Most ultrasound in-

struments are capable of receiving or sens-

ing these high-frequency ultrasound sound

waves within a frequency range of 20 kHz

to 100 kHz. For ultrasonic leak detection, an

ultrasound instrument that has frequency

tuning capability is recommended, and the

suggested frequency setting is 40 kHz. For

ultrasound instruments that are on a fixed

frequency or where frequency tuning is not

a feature, 38 kHz is usually the frequency

setting at which the instrument is fixed.

There are different sources of high-frequen-

cy sound that these ultrasound instruments

detect. For compressed air and compressed

gas leak detection, the source of the ultra-

sound is turbulence.

AIRBORNE ULTRASOUND & COMPRESSED AIR LEAK DETECTIONOnce an ultrasound instrument that will be

used for compressed air leak detection has

been selected, the planning of the com-

pressed-air survey can begin. One thing to

keep in mind while scanning for compressed-

air leaks out in the facility is the fact that high-

frequency sound is very low-energy. Because

it is low-energy, the sound will not travel

through solid surfaces but rather will bounce



and reflect off of solid surfaces. That’s why it

is important to scan in all directions with the

ultrasound instrument and adjust the instru-

ment’s sensitivity. Adjusting the sensitivity

and scanning in all directions will help pin-

point the location of the compressed air leak.

Once the general area of the compressed air

leak has been located, most ultrasound instru-

ments will come with a focusing probe that

can be slipped over the end of the airborne

scanning module to narrow the field of view

and more precisely identify the leak’s loca-

tion. This method of compressed-air leak

detection using ultrasound is commonly re-

ferred to as the “gross to fine” method.

The logistics of the leak detection route

should now be considered. Performing a

walk-through before the inspection is highly

recommended. The inspector should use this

as an opportunity to determine the specific

zones or areas where compressed air is being

used. Blueprints of the compressed air piping

are also a handy resource when conducting

the initial walk-through. When performing the

initial walk-through, note any safety hazards

and areas where accessibility to the test area

may difficult or may require the use of lad-

ders, extra PPE, or access to locked areas.

Also make note of any obvious signs of com-

pressed air misuse, potential areas of leak-

age, and improper piping installations. Not-

ing any areas of potential leakage or misuse

of compressed air (such as the use of air to

move parts/product, air knives, etc.) will help

eliminate confusion about what the inspector

is finding and help everyone become more

aware of where competing ultrasonic noise

is coming from. Part of the goal of the com-

pressed air leak survey could be to identify

areas where compressed air is being misused

and look for alternatives that could perform

the same function without having to use

costly compressed air.

It’s also necessary to determine the type of

leaks that ultrasound will be used to detect

– for example, pressure leaks in compressed

air or compressed gas systems, vacuum

leaks, or refrigerant leaks. After the initial

walk-through, select one area or zone to test

at a time. For consistency, it is a good prac-

tice to begin at the compressor (or supply)

side and then move to the distribution lines

and then to areas where the compressed air

is being used. As the compressed air leaks

are found with the ultrasound instrument, a

tagging system should be in place for tag-

ging the leak at the leak site. The tag should

have space for recording the leak number,

the pressure, the type of compressed gas, a

brief description of the leak location, and the

decibel level of the leak that was indicated

on the ultrasound instrument once the leak

location was confirmed. An estimated cost

of the leak may also be helpful in creating

awareness of the expense of compressed air

or compressed gas leaks.

DOCUMENTATION AND REPORTINGBeyond repairing the compressed-air leaks

that are found during the compressed-air



leak survey, the ultimate success of the

survey will rely largely on the reporting and

documentation of the compressed air leaks.

For documentation purposes, you may want

to consider using a leak survey app, which

can let the inspector easily document the

compressed air and compressed gas leaks

that are found, along with the associated

cost of the leaks.

When reporting the cost and CFM loss of

compressed air or compressed gas leaks,

it’s important to remember that these are

estimated costs. The cost of the compressed

air leaks will be based off of the decibel level

once the leak has been located, the cost per

kilowatt hour of electricity, and the pres-

sure at the leak site. Ideally, the pressure at

the leak site is best. For example, the com-

pressed air may start at the compressor at

120 psi, but where the air is actually being

used it may be regulated down to 75 psi.

Look for the nearest pressure gauge, or if

someone from the plant is available when

the leak survey is being conducted, have

someone who is familiar with the com-

pressed air system. For specialty gases such

as helium, nitrogen, or argon, the cost of the

compressed gas leak is based off the decibel

level reading at the confirmed leak loca-

tion, the pressure, and the cost of the gas

as a dollar amount per thousand cubic feet.

When noting the decibel level readings from

the ultrasound instrument, and for the ultra-

sonic leak report to be as accurate as pos-

sible, the inspector should note the decibel

level readings from the ultrasound instru-

ment approximately 15 inches away from the

confirmed leak location. If the decibel level

readings are taken too close to the leak loca-

tion, the report likely will overestimate the

cost and CFM loss of the leak. Several inde-

pendent studies have compared ultrasound

leak survey reports to actual energy savings,

and they have found that an ultrasound leak

survey is within 20% of the actual savings of

the compressed air leaks. When done cor-

rectly, an ultrasound compressed-air leak

survey can have tremendous payback in a

short period of time – once the leaks have

been repaired, of course.

Compressed air is an expensive utility

whose maintenance and cost is gener-

ally taken for granted. A successful com-

pressed-air leak survey depends on having

the right ultrasound instrument for the

survey’s needs, proper training of personnel

who will perform the survey, planning for

how the survey will be performed by doing

an initial walk-through, documentation of

the leaks and the associated costs, and ini-

tiation of repairs once the leaks have been

identified. Through proper documentation

and reporting, an ultrasonic compressed-air

leak survey can show tremendous payback

and energy savings without a significant

capital expenditure.

Adrian Messer, CMRP, is manager of U.S. operations

at UE Systems (www.uesystems.com). Contact him at

[email protected].

operations

UPmaintenance costs

DOWN

UltrasoundConditionMonitoring

when

FAILURE IS NOT AN OPTION

UE SYSTEMS PROVIDESCOST EFFECTIVESOLUTIONS FORPLANT RELIABILITY

EARLY WARNINGtest, monitor, trendand report conditions

TRAINING COURSESranging from certified classes to convenient on-line training

A WEBSITEfull of educational information to advance professional competence

CONFERENCESReliable Asset Worldand Ultrasound World

CONTACT US: T: 1-800-223-1325

E: [email protected]

SAVE THE DATE!May 14-17, 2019

Clearwater Beach, FLUltrasound World & Reliable Asset World

ADDITIONAL RESOURCESBusiness Analytics and Reliability Centered MaintenanceDescription: Recent trends show that more and more manufacturers

are looking to expand in the US or move capacity back from overseas.

To do this, companies will have to look for new, smarter ways to improve perfor-

mance, increase machine reliability, maximize workforce effectiveness and increase

uptime. This white paper explains how business analytics practices and reliability cen-

tered maintenance programs go hand and hand to reduce repair costs and streamline

the production workflow.https://software.response.e.abb.com/SuiteGenerationDigitalDisplayAds_Whitepaper?utm_campaign=2018Q4DisplayAds&utm_

medium=advertisement&utm_source=Plant%20Services&utm_content=Whitepaper

3 Top Measurement Technologies in Predictive MaintenanceAnalysts walk the plant floor using their human senses

(well, maybe not taste) to detect and start to diagnose machine health problems.

Technology can help automate some of that process, but there are

so many options when it comes to sensors. This introductory paper

covers three common measurement technologies that analysts use to

cross-diagnose machine failure.

https://blog.ifsworld.com/2018/05/eam-and-erp-an-increasingly-critical-partnership/

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detectors let you measure any degree of misalignment in a single

pass, regardless of distance and coupling size.https://

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UE Systems 4CastThe UE Systems 4Cast is a smart alert system that records data and sounds contin-

uously, issues alarms, sends data and sound samples to DMS software for analysis

and reporting. Data along with sound samples can be reviewed and analyzed to

determine the condition of a bearing just before, during and after a change in alarm status. This provides

important information to help understand what happened and when

it happened. Permanently installed transducers continuously monitor

bearing condition 24 hours a day, 7 days a week. All data is stored lo-

cally. Should a change in condition occur and a pre-established

alarm level is entered, the system, via the plant’s Ethernet will

issue an alarm notification, enter data and sound samples into

DMS software until the alarm condition has been reversed.



www.plantservices.comm • www.smartindustry.com

TECHNOLOGY REPORT 48

ADDITIONAL RESOURCES

UE Systems 4Cast

The UE Systems 4Cast is a smart alert system that records

data and sounds continuously, issues alarms, sends data and

sound samples to DMS software for analysis and reporting .

Data along with sound samples can be reviewed and analyzed to determine the condition

of a bearing just before, during and after a change in alarm status . This provides important

information to help understand what

happened and when it happened . Perma-

nently installed transducers continuously

monitor bearing condition 24 hours a day,

7 days a week . All data is stored locally .

Should a change in condition occur and

a pre-established alarm level is entered,

the system, via the plant’s Ethernet will

issue an alarm notification, enter data and sound samples into DMS software until the alarm

condition has been reversed . Learn more by viewing this webinar .

http://www .uesystems .com/training/introduction-ue-systems-4cast

https://www.gtipredictive.com/

https://www.plantservices.com/assets/Media/1903/top-3-measurement-technologies-predictive-maintenance.pdf

https://www.rotalign-touch.rocks/lp/rotalign_touch/page.en.html

http://www.uesystems.com/training/introduction-ue-systems-4cast

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