Improved Fault Diagnosis of Roller Bearings Using an Equal-Angle Integer-Period Array Convolutional Neural Network

This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure t...

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Bibliographic Details
Published in:Electronics (Basel) 2024-04, Vol.13 (8), p.1576
Main Authors: Li, Lin, Yuan, Xiaoxi, Zhang, Feng, Chen, Chaobo
Format: Article
Language:English
Subjects:
Arrays
Artificial neural networks
Bearings
Big Data
Classification
Cluster analysis
Data points
Deep learning
Fault diagnosis
Fuzzy logic
Integers
Matrices (mathematics)
Methods
Neural networks
Pattern recognition
Performance indices
Roller bearings
Rotation
Sampling techniques
Signal processing
Support vector machines
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Summary:This article presents a technique to carry out fault classification using an equal-angle integer-period array convolutional neural network (EAIP-CNN) to process the electrostatic signal of working roller bearings. Firstly, electrostatic signals were collected using uniform angle sampling to ensure the angle intervals between two adjacent data points stayed the same and the signal length was fixed to a pre-determined number of rotation cycles. Then, this one-dimensional signal was transformed into a two-dimensional matrix, where the component of each row was the signal in one period, and the ordinate value of each row represented the corresponding rotation period. Therefore, the row and column indexes of the matrix had a specific meaning instead of simply splitting and stacking the data. Finally, the matrixes were utilized to train the CNN network and test the classification performance. The results show that the classification rate using this technique reaches 95.6%, which is higher than that of 2D CNNs without equal-angle integer-period arrays.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13081576