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WIRELESS SMART SENSORS FOR CONDITION MONITORING IN INDUSTRIAL ENVIRONMENTS Dirk Mayer, Tobias Melz
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Fraunhofer LBF
Research Institute for Structural Durability and System Reliability
Main business areas
Transport and Automotive
Aerospace
Shipbuilding
Industry
Applied research for the industry and SMEs
Funded projects
Direct contract research and services
500 employees
Close research association with TU Darmstadt
System Reliability and Machine Acoustics
Macromolecular Chemistry
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Structural Durability
Smart Structures System Reliability Plastics
Fraunhofer LBF
Materials Processes System Integration Validation
Lightweight design
Function Integration
Safety Reliability
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Basic Technology Research
Research to Prove Feasibility
Technology Development
Technology Demonstration
System / Subsystem Development
System Test & Operations
Contract research
Services for industry
Cooperation with Fraunhofer LBF From fundamental research to marketable products
Publicly funded projects
EU, BMWi, BMBF,…
Initial research
Application of proven methods and procedures Structural and system
analyses Consultation Qualification of skilled staff
…
Applied research Bilateral R&E cooperation Feasibility studies …
TRL 1
TRL 2
TRL 3
TRL 4
TRL 5
TRL 6
TRL 7
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TRL 9
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Motivation
Smart systems for SHM / HUMS can help to enhance safety and optimize maintenance
Structural/ system integration and autonomous operation required
Energy scavenging enables application to widespan structures, freight trains, or other places which are hard to reach (rotating parts in manufacturing machines, …)
System has to be more reliable than the structure to be monitored
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Application Scenario Freight Car
Monitoring
Bearings
Brakes
Derailments
Wheel wear
Energy Supply
No on-board power available
Harsh environment
Solar cells etc. impossible
Fully encapsulated sensor system
Retrofitting has to be possible
Vibrations as preferred energy source
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Wireless self-powered Smart Sensors Components
Energy Harvesting system
Energy storage and management
Data acquisition
Signal processing
Wireless transmission
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Self-powered Smart Sensors Design Issues
Goal: Analyse and transmit data under restrictions of limited energy supply
Amount of generated energy versus size and weight of energy harvesting system
Computational effort for on-board data reduction versus power for wireless data transmission
Storage of electrical energy versus consumption for frequent measurement and analysis
Design of the overall system necessary
Analysis of interactions between components
Adjust the parameters regarding the actual application
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Reliable Energy Supply: Availability of Energy
Vibrations of the host structure may depend to the target application…
Compressor working in steady state
Vibrations at nearly constant frequency and amplitude
Resonant energy harvester can be tuned to the dominant frequency
Scavenged energy can be predicted
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Reliable Energy Supply: Availability of Energy
Vibrations of the host structure may depend to the target application…
Freight car operating in various conditions
Speed
Loading conditions
Track condition
Vibration levels and frequencies vary
Scavenged energy hard to predict
Feasibility studies in advance
Integrated design process necessary to reduce extensive field tests
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Feasibility Study and Design
Operational long term measurements
Estimation of scavenged energy
Set Up simplified models
Component design
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Data Analysis Balancing Signal Processing and Data Transmission
Transmission of raw data
Local data analysis and transmission of processed data
Number of Computations
Low
Volume of transmitted data
High
Number of Computations
High
Volume of transmitted data
Low
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System Level Evaluation with HITL
Analyse and optimize the interactions between the components
Assess reliability of operation
Avoid extensive field tests
Low number of hardware protos
Hardware-In-The-Loop Testing
Real-Time simulation of hardware (Harvester)
Sensor electronics
Test signals from initial operational measurments
Repeatable test conditions
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Components - Design to Reliability
Reliability of the harvester is crucial
Vibrations cause fatigue of the material and the piezo transducers
Assessment of reliability during the development
Fatigue tests
Environmental tests
Fibre Reinforced Plastics – high durability
Integrated piezo elements – protection
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Field test
Temperature measurements at the bearing (hotbox detection)
Installation of the system at the freight car
Instrumentation with additional accelerometers
Tests during real operation of the train
Successful validation of autonomous operation
Further system integration and improvement of robustness in subsequent projects
Energy Harvester
Energy Storage Sensor Node Temperature measured
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Acceleration measurements
Random Decrement data analysis
Current Challenge: Higher data rates
Vibration measurements to detect wear on wheel surfaces
Data acquisition (raw data)
1000 samples/s.
8 bit resolution min.
trigger
Transmission or storage of raw data impossible
0 50 100 150 200 250 300 350-200
-150
-100
-50
0
50
100
150
200
Wheel Angle [°]
Ave
rage
d V
ertic
al A
ccel
erat
ion
[m/s2 ]
Averaged Acceleration Series
Train Speed: 80 km/h
Efficient estimation of angle synchronized correlation functions
Storage of mean values
Analysis of deviations from initial state
ESZüG project, funded by BMBF
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Radio
Ultra Low Power µC MEMS-Accelerometer
Energy Harvesting Charging Storage Capacitor
Piezoelectric Generator
Ultra Low Power Wireless Smart Sensor
Current Challenge: Reliable and Highly Integrated Components
Compact and modular hardware
Robust against harsh railway environment
Temperature
Dust, Humidity
Vibration and shock
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Current Challenge: big data in sensor networks
Single sensor enables analysis of deviations from „healthy state“ Damage detection on individual specimen
Sensor Network enables deeper analyses
Correlation of failures with operational conditions Optimization of maintenance schedules
Relevant database by instrumentation of many (all) freight cars in operation
4..12 wheels per car X 200 000 freight cars (in Germany) Lifetime 40-50 years
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Current Challenge: Integration with Vibration Control
Energy transfer from mechanical oscillations induces damping
Proper scaling of the energy harvester
Integration of tuned vibration absorber and energy harvester
Potential applications
Bridges
Drivetrains in machinery
…
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Conclusions
Wireless self powered sensors enable new applications for sensing
Harsh environments where no cables are allowed
Retrofitting
…
Application is not „plug and play“…
Reliability is a crucial aspect
Methodic approach saves testing efforts
Optimization of components and performance
Big data from large number of sensors
Data reduction on board of the sensor to reduce communication
Big data methods enable deeper analyses
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Contact
Dr. Ing. Dirk Mayer
Head of department Reliability and System Integration,
Division Smart Structures
Fraunhofer Institute for Structural Durability and System Reliability LBF
Bartningstr. 47, 64289 Darmstadt, Germany
Phone: +49 6151 705-261, Fax: +49 6151 705-388
Prof. Dr.-Ing. Tobias Melz
Director (acting)
Fraunhofer Institute for Structural Durability and System Reliability LBF
Bartningstr. 47, 64289 Darmstadt, Germany
Telefon: +49 6151 705-252, Fax: +49 6151 705-388
Acknowledgments
The work presented here was supported by
• BMBF, projects EA-TSM and ESZüG
• State of Hesse, LOEWE center AdRIA
• European Commission, Marie Curie ITN EMVeM
