Data-based Distributed Fault Diagnosis for Adaptive Structures using Convolutional Neural Networks
| Autor: | Oliver Sawodny, Cristina Tarin, Michael Böhm, Bernd Bertsche, Andreas Gienger, Andreas Ostertag |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science 020208 electrical & electronic engineering Aerospace Engineering 02 engineering and technology Fault (power engineering) Convolutional neural network Reliability engineering 020901 industrial engineering & automation Control and Systems Engineering Automotive Engineering 0202 electrical engineering electronic engineering information engineering Reliability (statistics) |
| Zdroj: | Unmanned Systems. :221-228 |
| ISSN: | 2301-3869 2301-3850 |
| DOI: | 10.1142/s2301385020500156 |
| Popis: | Adaptive structures are able to react to environmental impacts and have become a promising approach in civil engineering to improve the load-bearing behavior of buildings. Since reliability and safety of building structures are major concerns, the detection and isolation of faults are essential. In this work, the data-based distributed fault diagnosis of sensor and actuator faults in an adaptive high-rise truss structure is investigated and compared to a centralized approach. The decomposition of the different subsystems is given by the hardware layout of the different sensor systems and actuators. The mechanical structure is modeled and extended by dynamic sensor and actuator models containing different faults. Based on the simulation model, different fault scenarios are generated and used for training a convolutional neural network with dropout regularization. It is shown that the distributed approach needs less training data and yields better classification results than the centralized approach due to a significant reduction of the complexity and dimensionality. |
| Databáze: | OpenAIRE |
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