IoT technology is the enabler for many radical innovations in data driven business models. Here are some thoughts on the challenges for industrial IoT application.

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Dirk Mayer
THE INTERNET OF SAFETY-RELEVANT
THINGS

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Miniaturization as an Enabler for the Internet of Things
 Small, low-power systems
 MEMS Sensors
 Low cost solutions
 Wireless communication
 Gather high amount of data
from integrated sensors
 Extraction of information by data
fusion
 Optimization of processes by
control and monitoring networks

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Daniel Wellers: Is this the future of the Internet of Things?

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Scenarios for Potential Added Value
State of the Art
Design of safety critical parts based on
experiences and past data
IOT Application:
Sensor integration in structural parts
 Acquisition of loads in real operation
 Analysis of damages and fatigue
Benefits
 Optimization of maintenance schedules
 Optimization of designs
 Optimization of operation

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Challenges for Industrial IoT Applications
Application Requirements
 Harsh environmental conditions
 High availability
 High lifetime of industrial equipment
 High amount of sensor data
Research Challenges
 Reliability
 Wireless systems/ smart sensors
 Low Power/ Self powered
 Model based design

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Reliable Functional Integration
Integration of sensors, electronics and actuators into structural components
 Protection against environmental loads
 Direct mechanical coupling (for actuators also cooling)
 Less number of parts
Fibre reinforced
plastics
Additive Manufacturing
polymer metal
Aluminum
die casting
Manufacturing Technologies investigated

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Integration of energy harvesting for autonomous
operation
Challenges
 Complex balancing of generation, storage and consumption of energy
 High level of uncertainty in environmental energy sources (vibration, solar, wind…)
Research Approach: Model based development process
 System simulation allows for evaluation of designs against target performance
 Next-generation Hardware-in-the-loop testing for less system tests in the field
 Rapid prototyping using additive manufacturing and modular electronics
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Operational
vibration
measurements
Testing of components
and subsystems
Rapid implementation of
prototypes
System simulation and
evaluation

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Smart Sensors with Integrated Data Analysis
Application scenario: Vibration monitoring in a railway environment (wheels)
 Data acquisition: 500 samples/sec., 16bit/sample
 1 train = 320 wheels >2,7GB/day raw data
Local data analysis for reduced communication bandwidth
 Efficient data compression: ok/not ok per wheel = 320 bit/day
 Low power signal processing (software, hardware)
 Decentralized extraction of relevant information
 Reconfigurable smart sensor hardware

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Perspective: Service-based Business Models
Products
• Machinery
• Components
• Fuels
Example: Selling produced parts
„Why behind the buy“
The customer job to
get done
Services
• Condition based
maintenance
• Pay per Use
• On demand – supply
• …

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Perspective: Data as a commodity
Raw data Analysis Prognosis Optimization
Value
 Increasing value by
 Application of AI techniques for data analysis
 Integration of analytics with physical insights and human expertise

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The Industrial Data Space Platform
Connecting Smart Services and the Internet of Things
B. Otto (2017): Industrial Data Space – Reference Architecture for Data Sovereignity
Knowledge grows when shared –Bhartrihari (450—510 C.E.)

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Experiences and Applications
Application Tasks
Road transport Monitoring of suspensions
Bridges Long term structural analysis
Wind energy
Structural health monitoring of tower and blades
A/C components Damage detection at CFRP structures with ultrasonic sensor
node
Large container ships Monitoring of rudders and drivetrains
Intralogistics Condition monitoring of forklift systems and cranes
High performance cars, E-mobility Function integration into CFRP parts, monitoring

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Further readings
 Bein, T., et.al. (2016) Maintenance-on-Demand Concepts for Commercial Vehicles: The MoDe Project, in Towards
Innovative Freight and Logistics (eds. C. Blanquart, U. Clausen and B. Jacob), John Wiley & Sons, Inc., Hoboken, NJ, USA.
 Bein, T.; Mayer, D.; Hagebeuker, L.; Bachinger, A.; Bassan, D.; Pluymers, B.; Delogu, M. (2016): Enhanced Lightweight
Design–First Results of the FP7 Project ENLIGHT. In: Transport Research Arena Conference-TRA. Poland: Warsaw.
 Lilov, M.; Siebel, T.; Contell Asins, C.; Schwarzhaupt, O.; Carli, V. (2016): Development of a Structural Health and Load
Monitoring System and its Implementation into an Aircraft Flying Demonstrator. In: 8th European Workshop on
Structural Health Monitoring (EWSHM 2016). Spain: Bilbao, July 5-8, 2016.
 Siebel, T.; Friedmann, A.; Strobel, W.; Guse, M.; Hartmann, A. (2016): Longtime monitoring with smart sensor networks:
the case of the Zwartewaterbrug. In: Bagger; Frangopol; van Breugel (Hrsg.): Life-Cycle of Engineering Systems:
Emphasis on Sustainable Civil Infrastructure: IALCCE. England: London, Taylor & Francis Group, 16.-19.10.2016, S. 1773–
1779. ISBN: 978-1-138-02847-0.
 Lilov, M., Siebel, T. (2015): An Electromechanical Impedance-Based Mobile System for Structural Health Monitoring and
Reliability Check of Bonded Piezoelectric Sensors. In: Smart Intelligent Aircraft Structures (SARISTU). Moskau, Russland:
Springer, S. 529–543.
 Mayer, D.; Stoffregen, H.; Heuss, O.; Thiel, J.; Abele, E.; Melz, T. (2015): Additive manufacturing of active struts for
piezoelectric shunt damping. In: Journal of Intelligent Material Systems and Structures.
 Lilov, M., Siebel, T. (2014): EMILIA – A compact Impedance Analyzer for Local Integrity Assessment. In: 9th International
Conference on Structural Dynamics EURODYN 2014. Portugal: Porto.
 Engel, A.; Friedmann, A.; Koch, M.; Rohlfing, J.; Siebel, T.; Mayer, D.; Koch, A. (2014): Hardware-accelerated wireless
sensor network for distributed structural health monitoring. In: SYSINT-SHM 2014. Deutschland: Bremen.
 Mayer, D.; Stoffregen, H.; Heuss, O.; Pöllmann, J.; Abele, E.; Melz, T. (2014): Additive Manufacturing of Active Struts for
Piezoelectric Shunt Damping. In: Journal of Intelligent Material Systems and Structures.
 Buff, H.; Kloepfer, J.; Hansmann, J. (2013): Automatic Damage Detection in Vehicle's Shock Absorbers. In: Structural
Engineering Dynamics (ICEDYN). Portugal: Sesimbra.
 Koch, M.; Kurch, M.; Mayer, D. (2012): Methodical Design Approach and Field Test of a Self-Powered Train Hot Box
Detector. In: International Journal of Railway Technology. Jahrg. 2012, Nr. 1(4), S. 19–38. ISSN: 2049-5358.