Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCN...

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Veröffentlicht in:	PloS one 2021-08, Vol.16 (8), p.e0256287-e0256287
Hauptverfasser:	Yan, Jing, Liu, Tingliang, Ye, Xinyu, Jing, Qianzhen, Dai, Yuannan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Analysis Artificial neural networks Bearings (Machinery) Biology and Life Sciences Computer and Information Sciences Convolution Deep learning Embedding Engineering and Technology Fault diagnosis Humans Industrial applications Industrial Internet of Things Intelligence Internet of Things Internet of Things - standards Laboratories Lightweight Machinery Magneto-electric machines Mathematical models Medical diagnosis Methods Models, Theoretical Neural networks Neural Networks, Computer Parameter robustness Physical Sciences Power Principal components analysis Research and Analysis Methods Robustness Rotating machinery Signal processing Social Sciences Stochastic Processes Stochasticity Structure Vibration Wavelet Analysis Wavelet transforms Weight reduction
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