Fault Cause Assignment with Physics Informed Transfer Learning
To maintain successful operation, the field of health monitoring, fault detection and diagnosis plays a key role. Within the scenarios of system faults, locating a fault in a complex system consisting different components are one of the key challenge in fault detection. In this context, diagnosis of...
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| Published in | IFAC-PapersOnLine Vol. 54; no. 20; pp. 53 - 58 |
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| Format | Journal Article |
| Language | English |
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Elsevier Ltd
2021
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| Abstract | To maintain successful operation, the field of health monitoring, fault detection and diagnosis plays a key role. Within the scenarios of system faults, locating a fault in a complex system consisting different components are one of the key challenge in fault detection. In this context, diagnosis of system faults originated from actuator and sensor is addressed to perform fault source separation. Physics of the underlying dynamics are investigated using only input and output data streams along with a purely data-driven technique, dynamic mode decomposition with control (DMDc) without a need for system model. Then time-frequency representation of the dynamic modes are obtained using continuous wavelet transform (CWT) and utilized in deep convolutional neural network (DCNN) to classify three scenarios for the case study, namely; nominal, actuator bias and sensor bias fault scenarios. Instead of training a DCNN structure from scratch, GoogLeNet structure presented for image classification is utilized as a standard methodology of transfer learning process. Finally, results of the image classification methodology are presented. |
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| AbstractList | To maintain successful operation, the field of health monitoring, fault detection and diagnosis plays a key role. Within the scenarios of system faults, locating a fault in a complex system consisting different components are one of the key challenge in fault detection. In this context, diagnosis of system faults originated from actuator and sensor is addressed to perform fault source separation. Physics of the underlying dynamics are investigated using only input and output data streams along with a purely data-driven technique, dynamic mode decomposition with control (DMDc) without a need for system model. Then time-frequency representation of the dynamic modes are obtained using continuous wavelet transform (CWT) and utilized in deep convolutional neural network (DCNN) to classify three scenarios for the case study, namely; nominal, actuator bias and sensor bias fault scenarios. Instead of training a DCNN structure from scratch, GoogLeNet structure presented for image classification is utilized as a standard methodology of transfer learning process. Finally, results of the image classification methodology are presented. |
| Author | Chen, YangQuan Guc, Furkan |
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| Cites_doi | 10.1016/j.ins.2020.06.060 10.1109/TIE.2017.2774777 10.1016/j.ymssp.2018.02.046 10.3390/s18061972 10.1137/15M1013857 10.1109/CVPR.2015.7298594 10.1109/JSEN.2010.2055236 10.1016/j.enbuild.2019.109689 10.1109/ACCESS.2020.3037117 10.1109/JSEN.2015.2497277 10.1016/j.jcp.2018.10.045 10.1115/1.4002877 10.1109/DCOSS.2013.52 10.1109/ACCESS.2018.2888842 10.2514/1.J059943 10.1016/j.arcontrol.2020.08.003 10.1017/S0022112010001217 10.1109/CVPR.2016.90 10.1016/j.ymssp.2017.09.013 |
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| Keywords | Health monitoring Source Separation Data-Driven Approaches Transfer Learning Fault detection diagnosis Dynamic Mode Decomposition |
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| SubjectTerms | Data-Driven Approaches diagnosis Dynamic Mode Decomposition Fault detection Health monitoring Source Separation Transfer Learning |
| Title | Fault Cause Assignment with Physics Informed Transfer Learning |
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