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- W3213017966 abstract "Fault diagnosis has a great significance in preventing serious failures of rotating machinery and avoiding huge economic losses. The performance of the existing fault diagnosis approaches might be affected by two factors, i.e., the quality of fault features extracted from monitoring signals and the capability of fault diagnosis model. This paper proposes a new fault diagnosis method combined mel-frequency cepstral coefficients (MFCC) with a designed parallel multi-fusion convolutional neural network (MFCNN) Specifically, a MFCC-based feature extraction method is defined to reduce the noise components in monitoring signal of rotating machinery and extract more useful low-frequency fault information for downstream task. Furthermore, a novel MFCNN is designed to enrich the high-level features after each convolution operation by using multiple activation functions, so as to improve the quality of the obtained fault features. Meanwhile, a new parallel MFCNN is constructed by using a defined structural ensemble operation to improve its diagnostic performance in different noise environments. Two typical bearing and gearbox failure datasets are applied to evaluate the performance of the proposed fault diagnosis method. The experimental results indicate that the proposed parallel MFCNN has the better diagnostic performance than other methods." @default.
- W3213017966 created "2021-11-22" @default.
- W3213017966 creator A5022583120 @default.
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- W3213017966 date "2022-09-01" @default.
- W3213017966 modified "2023-09-29" @default.
- W3213017966 title "Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments" @default.
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- W3213017966 doi "https://doi.org/10.1016/j.isatra.2021.10.023" @default.
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