Wireless smart sensors enable new applications for condition monitoring and are a key element of the internet of things (IoT). We discuss the major challenges when implementing this technology in real applications, like reliability, energy supply and the need for on-board data analysis. The presentation was first given at the Smart Factory Innovation Forum in Munich

1. © Fraunhofer
WIRELESS SMART SENSORS FOR
CONDITION MONITORING IN INDUSTRIAL
ENVIRONMENTS
Dirk Mayer, Tobias Melz

2. © Fraunhofer
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 ReliabilityPlastics
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
TRL 8
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 [°]
AveragedVerticalAcceleration[m/s
2
]
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

21. © Fraunhofer
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
dirk.mayer@lbf.fraunhofer.de
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
tobias.melz@lbf.fraunhofer.de
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