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Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes

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Abstract

In industrial processes, the noise and high dimension of process signals usually affect the performance of those methods in fault detection and diagnosis. A predominant property of a fault diagnosis model is to extract effective features from process signals. Wavelet transform is capable of extracting multiscale information that provides effective fault features in time and frequency domain of process signals. In this paper, a new deep neural network (DNN), multichannel one-dimensional convolutional neural network (MC1-DCNN), is proposed to investigate feature learning from high-dimensional process signals. Wavelet transform is used to extract multiscale components with fault features from process signals. MC1-DCNN is able to learn discriminative time–frequency features from these multiscale process signals. Tennessee Eastman process and fed-batch fermentation penicillin process are adopted to verify performance of the proposed method. The experimental results demonstrate remarkable feature extraction and fault diagnosis performance of MC1-DCNN and show prosperous possibility of applying this method to industrial processes.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 71777173), Action Plan for Scientific and Technological Innovation of Shanghai Science and Technology Commission (No.19511106303) and Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Mechanical Engineering, Tongji University, Shanghai, 2010804, People’s Republic of China

    Jianbo Yu & Chengyi Zhang

  2. School of Economics and Management, Tongji University, Shanghai, 200092, People’s Republic of China

    Shijin Wang

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  1. Jianbo Yu
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Correspondence to Jianbo Yu.

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Yu, J., Zhang, C. & Wang, S. Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput & Applic 33, 3085–3104 (2021). https://doi.org/10.1007/s00521-020-05171-4

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