Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors

Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much sl...

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Vydáno v:Quality and reliability engineering international Ročník 40; číslo 2; s. 819 - 837
Hlavní autoři: Yang, Cheng, Yuan, Yannan, Wang, Fu, Li, Jueying, Li, Ang, Min, Yuan, Zhang, Qiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.03.2024
Témata:
Algorithms
Circuit design
Circuits
Control equipment
Decomposition
drive circuit
Driver circuits
Electronic components
empirical mode decomposition (EMD) algorithm
Fault diagnosis
Fault location
Faults
Feature extraction
Machine learning
Nuclear reactors
Phase current
sparse convolutional autoencoder (SCAE)
ISSN:0748-8017, 1099-1638
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Shrnutí:Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co‐exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three‐phase current signals of drive circuits. An SCAE‐based feature extractor is constructed to capture high‐dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors.
Bibliografie:ObjectType-Article-1
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ISSN:0748-8017
1099-1638
DOI:10.1002/qre.3452