Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Improved of Fault Diagnosis Using 1D-Convolutional Neural Network Model

Version 1 : Received: 20 November 2020 / Approved: 23 November 2020 / Online: 23 November 2020 (09:22:43 CET)

A peer-reviewed article of this Preprint also exists.

Chen, C.-C.; Liu, Z.; Yang, G.; Wu, C.-C.; Ye, Q. An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model. Electronics 2021, 10, 59. Chen, C.-C.; Liu, Z.; Yang, G.; Wu, C.-C.; Ye, Q. An Improved Fault Diagnosis Using 1D-Convolutional Neural Network Model. Electronics 2021, 10, 59.

Journal reference: Electronics 2020, 10, 59
DOI: 10.3390/electronics10010059

Abstract

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

Subject Areas

1D-CNN; fault diagnosis; rolling bearing; vibration signal; single load; different load

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