Fault Diagnosis of Rotating Machinery Based on 1D-2D Joint Convolution Neural Network

Convolutional neural network has been widely used in fault diagnosis of mechanical devices. In particular, a 2-D convolutional neural network requires manual selection of multiscale transformation to transform vibration signal into the 2-D structure. Although a 1-D convolution neural network can dir...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 70; H. 5; S. 5277 - 5285
Hauptverfasser: Du, Wenliao, Hu, Pengjie, Wang, Hongchao, Gong, Xiaoyun, Wang, Shuangyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Back propagation networks
Continuous wavelet transforms
Convolution
Convolutional neural networks
Datasets
Diagnostic systems
Entropy (Information theory)
Error propagation
Fault diagnosis
Feature extraction
Gearboxes
joint convolutional neural network (JCNN)
Mechanical devices
Neural networks
Rotating machinery
Signal processing
Transforms
Two dimensional models
Vibration
Vibrations
Worm gears
ISSN:0278-0046, 1557-9948
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Zusammenfassung:Convolutional neural network has been widely used in fault diagnosis of mechanical devices. In particular, a 2-D convolutional neural network requires manual selection of multiscale transformation to transform vibration signal into the 2-D structure. Although a 1-D convolution neural network can directly use the vibration signal for convolution processing, it cannot make full use of the nonlinear information in the 1-D space. In order to make full use of the advantages of 1-D and 2-D convolutional neural networks, in this article, we develop a one-dimension in tandem with 2-D joint convolutional neural network (1D-2D JCNN) for rotating machinery fault diagnosis. More specifically, 1-D convolution is employed to adaptively obtain the multiscale feature vectors of the vibration signal, and these feature vectors are constructed into 2-D maps, and then these 2-D vectors are used as the input of the 2-D convolutions neural network. Take the cross-entropy loss function as the loss function and use the error back propagation algorithm to optimize the filter parameters of the 1D-2D JCNN model to obtain the final fault diagnosis model. Using the motor bearing dataset and the worm gearbox dataset, the experimental results show the excellent classification performance of bearings and gears under different working conditions. The average diagnostic accuracy of ten runs on Case Western Reserve University bearing dataset is 99.92%, and the variance is 3.96e-6. The average diagnostic accuracy of ten runs on the worm gear dataset is 99.82%, and the variance is 7.82e-6. Compared with the traditional fault diagnosis model and the latest convolution neural network method, the 1D-2D JCNN shows better diagnosis performance.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3181354