A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Abstract

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network (DSRCNN), is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB's life cycle, and facilitate the application of intelligent fault diagnosis system.

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

“…Since the optimization algorithm has randomness, the result of each iteration will be different, so the optimal parameter combination [α, K] is obtained by averaging the results after 20 times of optimization in this paper. Among them, the value range of penalty factor α is [0,3000], the value range of decomposition modulus K is [3,8] and is an integer, and the maximum number of iterations is 20. The optimization results are shown in table 3.…”

“…Therefore, implementing an intelligent evaluation and detection of CB functioning state is important from a theoretical and engineering standpoint. A multinational survey [2][3][4] found that mechanical failure accounts for approximately 59.9% of CB failures, with operating mechanism failure being the primary cause of CB mechanical failure.…”