Sensors Expo 2013: Condition Based Maintenance, Evigia Systems

Integrated Wireless Multi-Sensor System for Machine Health and Condition-Based Maintenance

Dr. Navid Yazdi CEO, Evigia Systems, Inc.

Sensors Expo 2013 Evigia Systems

Condition-Based Maintenance (CBM)

Reactive maintenance

Preventive Maintenance

Degradation / Alarm-Based Maintenance

Predictive Maintenance

Maintenance Strategies

CBM utilizes information about the present degradation in conditions and performance, and their projected evolution to minimize the downtime.


Condition Based Maintenance

• CBM Goals:

Automated Fault Diagnosis & Fault Isolation

Determine Remaining Useful Life of the system

Enhance predictability and productivity

Improve operation profitability

sensors Feature Extraction Classification Diagnostics Prognostics Actionable

Information

CBM System Components & Information Flow


Machine Prognosis Approaches

• Model-Driven – Employ the system physics

model or simulations under normal & degraded conditions

• Data-Driven – Pattern recognition &

machine learning applied to monitored system data

– Detect changes, devise trajectory and estimate RUL

• Hybrid

Prognosis Accuracy

Increase # of Sensors

& Types

Sensor Data Fusion

Customized Data

Processing


Advanced CBM – Benefits

• Improved reliability & safety

• Reduced downtime

• Synchronized maintenance

• Near-optimal parts logistics

• Reduced life-cycle costs

• Increased profitability


Advanced CBM – Challenges • Cost!!

– Adding embedded sensors, wiring, instrumentation

– Customization of algorithms

• Sensors disruption of process or affect performance

• CBM sensors reliability

– Accuracy over long-term

– Additional failure points & maintenance

Operations apply preventive, CBM , and reactive maintenance Wireless multi-sensor nodes shift balance to CBM across a larger set


Wireless Multi-Sensor Node • Flexible and cost effective network • Distributed & large-scale sensing • Less disruptive and easier to embed • Retrofit existing equipment • Reduced size, weight, power and cost • Energy harvesting combined with

improved sensor node energy efficiency • Improved link reliability in industrial

environments – ISO18000-7 low-data rate apps with robust link

and low interference – Variations of IEEE 802.15.4 PHY including

ISA100.11, WirelessHART

Millimeter scale energy harvester Courtesy: Univ Michigan-E. Aktakka, K. Najafi

Evigia RFID-pressure-temperature sensor


CBM Sensors

Gather high-reliability & accurate in-situ information

Sensor Functions

Environment Structural characteristics Dynamics Performance Monitoring Oil quality & corrosion

Sense Parameters

Temperature, vibration, shock, pressure, acoustic levels, strain, stress, voltage, current, humidity levels, contaminant concentration, usage frequency, usage severity, usage time, power, heat dissipation, corrosion, oil quality

Sensor Data Features

Magnitude, variation, peak level, and rate of change

Sensor Precision

Initial specs, calibration, low-drift, multi-sensor data fusion


CBM Sensor Example: Chilled-Water System

Source: Secy. to CME/WCR/JBP, 2006 - irsme.nic.in

Parameter Purpose

Water Flow Rate Chiller water flow

Diff. Temperature Heat transfer, tube fouling

Diff. Pressure across Pump & Evaporator

Track pump performance, Chiller tube conditions

Motor Current Motor conditions

Vibrations Condition of pump & structures

Oil condition & wear particle analysis

Lubricant, gears,

Thermography scan Motor control & electronics

Wall-thickness ultrasound

Corrosion & erosion check

Eddy current test Locate leaking tubes

Airborne ultrasonic Air leak


CH 53-K Chopper CBM • NAVAIR state-of-the-art heavy lift

chopper by Sikorsky (UT)

• Increased complexity while require increased reliability and longer maintenance cycle

• Light-weight and efficient (Low SWaP+C)

• CBM Requirements: – Local & global structural damage detection

– Loads monitoring

– Environmental monitoring

– Fleet Management


Multi-Sensor Node: Baseline Technology

• 10’s-100’s discrete components including sensors assembled at board level

– Larger size (inches)

– Higher Cost ($10’s-100’s)

– Larger power dissipation & shorter battery life ( months)


Requirements and Challenges • Embedded multi-sensors and equipment condition monitoring

– 5-10x smaller size & cost

• Multiple sensors – Strain, Temperature, Vibration,

Shock/Mechanical sensors, Humidity

• Size < 0.5 cubic inches

• Weight < 0.5oz

• Energy efficient < 300μW

• Cost $5-20

• Standard based rugged wireless data link


Solution • Chip-scale multi-sensor prognostic sensor system

– Integration of energy efficient sensors and electronics on chip

– Mass-volume manufacturing technologies

Sensor Interface & Control Circuitry

Digital Interface Bus

Digital Memory

SPI/I2C Serial Interface

Sensor Event Management &

Real-Time Clock

IC-MEMS Micropackaged Prognostic Sensor Chip

Shock/Acceleration Sensor

Strain Sensor

Vibration Sensor

Temperature Sensor

Low-power ICs MEMS Sensors Wafer-Level Micropackaging


Features, Advantages, Benefits Feature Advantage Benefit

Multiple sensors (Strain, Vibration, Temperature, Shock)

Multiple condition parameters Provide full suite of parameters

Compact size (<0.5 cubic inches) Reduces size by 10x Enable embedding of the prognostic sensor in equipment

Light weight (< 0.5 ounces) Reduces weight by 10x Enable embedding of the prognostic sensor in equipment

Energy-efficient (20-300 micro watts average depending on mode of operation)

Reduces power draw Extend module power up option, Extend battery life

Manageable cost ($5 to $20, vary by sensor mix) Reducing cost by 10-50x Cost savings; Broaden usage in

submodules and systems

Optional Wireless link compatible with the DoD infrastructure

Ease of integration in military systems

Leverage from the installed infrastructure; Reduce the installation and operation costs


Prognostic Sensor Microsystem

TemperatureSensor

Wired Serial Data Interface

HumiditySensor

StrainSensor

VibrationSensor Event-Management

And Logic Control

Memory

MUX

ShockSensor

CapacitanceReadoutand ADC

Wireless Data Link

Prognostic Sensor Module

Prognostic Chip PMSC-A

Flash Card Memory

1Gb+

• Strain-Vibration-Shock-Temperature sensors with wireless data link

• Target military rotary wing aircraft (CH-53), and other defense and industrial equipment prognostics applications


Multi-Sensor Wafer

Shock Sensor

Strain Sensor

Vibration Sensor


Sensor Specifications

Sensor Range Resolution Accuracy

Strain 1-1000µε 1µε 1% of full-scale

Vibration ±10g 50mg 2% of full-scale

Shock/Impact 50g-800g depends on the array design

5% of shock switch threshold

Temperature -55 ºC to 85ºC 0.1 ºC 0.5ºC

Humidity 0% to 100% RH 0.1%RH 3%RH


Prognostic Sensor ASIC

Evigia Multi-Sensor Prognostics ASIC


Specifications Sensor

Sensor types supported Low bandwidth: temperature, strain, shock; High-bandwidth: Vibration Sensor integration 4 shock, 1 strain, 1 vibration, temperature External Sensor Support 11 shock, 1 strain, 2 vibration

Memory Internal EEPROM Size 16 kbit (167 to 375 low-bandwidth sensor events)

External Flash Supported 2 Gbits (> 44 million low-bandwidth sensor events, 134 million vibration sensor samples [70 minutes total])

External Flash Interface Serial (SPI) , 2 master SPI flash memory interfaces Timing

Input Clocks 32kHz RTC Length 17 years RTC Resolution 30 bits

Performance ADC Resolution 11-bit

Sampling rate Low-power mode: 0.125Sec-4hrs Vibration Active: Up to 10kSPS

Digital Interface Communications Interface 4-wire SPI slave Supply

Voltage 2.5V - 3.3V

Current Low-power mode (@3.3V): <30µA Instantaneous Sensors , On-Chip NVM <320µA Instantaneous , Average : 1 sample per minute: <2µA Vibration Active: <100µA not including external flash memory

Battery Life [500mAHr cell] >25 years in low-power mode , 1 Samp/min (not including self-discharge)


Development and Deployment

• Energy-efficient integrated strain sensor, vibration sensor, and 3-axis mechanical shock sensor are complete

• Manufacturing process being qualified (MRL4)

• Sensor & electronics IC rev 1 (TRL5)

• Single chip sensor-electronics Q1’14 (TRL 6)

• Module with wireless link Q2’14 (TRL6)


CBM Case Study: Army TACOM

Condition Based Maintenance Plus, Return on Investment Analysis Ken Fischer (01/12/11) US Army RDECOM-TARDEC

• Army TACOM Life Cycle Management Center is responsible for the sustainment of all ground vehicle platforms and supporting equipment.

• CBM+ Initiative to transition military from traditional reactionary and time-based preventative maintenance to deliver maintenance only upon evidence of need (CBM).

• CBM+ potential savings in: – Reduction in direct costs of part replacement and associated

maintenance labor costs

– Reduction in inspection times, reduced logistic down times, and ability to change the level of repair for diagnosable failure modes

– Reduction in maintenance costs due to reduced mean time to repair (MTTR)

– Reduced mean logistics down time (MLDT) due to improved forecasting and advanced reporting of impending failures

– Savings from logistics efficiencies as repair parts are better staged where most needed.

HEMTT Heavy Transport Introduced 1982 13,000 Vehicles currently in service

Operations & Support (O&S) costs account for 60% – 80% of the total lifetime cost for military platforms.


CBM Case Study: Army TACOM

Condition Based Maintenance Plus, Return on Investment Analysis Ken Fischer (01/12/11) US Army RDECOM-TARDEC

• Sensor data acquisition system for engine, transmission, tires and batteries – $15k estimated per vehicle for “enabling technologies” of sensors, data acquisition/processing

and infrastructure

This initial TACOM CBM study identified $26M in potential cost savings (direct and avoided costs) for an analysis of 1045 vehicles

• DoD is looking for COTS solutions for Wireless Diagnostic Sensors (WDS) to support this activity

• Lowering the costs of the enabling sensor technologies will have a significant ROI impact when implementing a CBM initiative over a large number of vehicle systems


CBM Case Study: Army AMCOM

AMCOM Condition Based Maintenance, AMC CBM Summit 7/18/08 US Army AMCOM, Gary Nenninger

• CBM Digital Source Collection (DSC) system for rotor aircraft, monitors 50+ sensor inputs – UH-60, AH-64, CH-47 Aircraft outfitted with DSC sensors and communications

– Monitoring rotor characteristics, engine health, drive train health, structural health and exceedance (over-temp, over-torque, etc.) conditions

• Sensor weight, connectivity, and robustness are major concerns in implementation – Current CH-47 DSC sensor infrastructure is > 45 pounds




• Results of AMCOM one-year CBM implementation (350 aircraft deploying DSC system):

– Significant savings of >11,000 maintenance (MMF) and ~1,300 test flight (MTF) hours

– Extended component life and improved confidence in reliability

– An estimated $101M in one-year direct and indirect cost savings

– Improved mission readiness and hours flown, adding more available aircraft to operations

5% increase in Fully Mission Capable (FMC) status is

equivalent of 1.5 more aircraft. 1,431 increased Hours

Flown is equivalent of 2 additional aircraft at OPTEMPO

MMH: Ma in tenanc e Man-Hou rs MTF : Ma in tenanc e Tes t F l i gh t

AMCOM One-Year CBM Implementation

~ $101M in savings from CBM trial

Impact of equipping aircraft with DSCs




• True benefits: avoidance of catastrophic component failure leading to loss of life and aircraft – Impending failure of specific AH-64 rotor bearing identified via CBM data, confirmed on teardown

2 Pilots saved $16M aircraft saved


Prognostics Case Study: Caterpillar

Multi-sensor Equipment Health Diagnosis And Prognosis European Journal of Operational Research, 2007

• Prognostics and Health Management (PHM) study, analyzing health-state probability to predict the useful remaining life of Caterpillar hydraulic pump components.

• Diagnostics is the assessment of the current and past health of a system based on observed symptoms, and prognostics is a predictive assessment of the future health

• Uses novel multi-sensor fusion approach to adjust the weight or importance assigned to any sensor in the PHM analysis, yielding multi-dimensional model for PHM predictions

– Temperature, vibration, oil particle count sensors

Advances in networked multi- sensor technology enable corresponding advances in applications of diagnostics and prognostic health management

• Multiple sensors may have cooperative, complimentary or competitive predictive qualities, PHM studies can take advantage of multi-sensor characteristics

• Weighted multi-sensor fusion approach shows accuracy of PHM predictions was increased by 18.5% over single-dimensional model for Caterpillar 9J5082

Multiple hydraulic pumps and drive engine on test stand


Case Study: Nuclear Material Storage • Hazardous material transport and storage presents a significant maintenance challenge

– Nuclear and radioactive materials, hazardous waste products, corrosive or toxic chemicals, volatile bulk process ingredients

• CBM enabled by custom container-mounted multi-sensors to monitor transit/storage location, seal breakage, package impact, temperature, humidity, radiation levels, and presence of gases and certain biological elements

Annual inspection and monitoring direct cost savings of over $2,000 per container

• CBM system lowers life-cycle management costs, reduces the requirements for manned surveillance and inspection, minimizes the potential hazardous exposure for plant personnel, and extends maintenance cycles of storage and transport vessels


About Evigia Systems

Contact: Dr. Navid Yazdi CEO, Evigia Systems, Inc. [email protected] www.evigia.com Twitter: @EvigiaRFID

3810 Varsity Drive Ann Arbor, MI 48108

(734) 302-1140

• Evigia Systems (Ann Arbor, MI) develops wireless sensing, identification and tracking products and integrated solutions for military, security, and commercial applications

• Expertise in MEMS-based sensor development, integrated multi-sensors, robust wireless communication, ASIC design, and Active RFID systems

• Focus on delivering sensing technology with smaller form factors, higher energy efficiency, increased functionality, and lower production costs

Sensors Expo 2013: Condition Based Maintenance, Evigia Systems

Engineering

Transcript of Sensors Expo 2013: Condition Based Maintenance, Evigia Systems