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Materials 2017, 10(7), 790; doi:10.3390/ma10070790

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

1
School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
2
School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Received: 9 June 2017 / Revised: 6 July 2017 / Accepted: 7 July 2017 / Published: 12 July 2017
(This article belongs to the Special Issue Structural Health Monitoring for Aerospace Applications 2017)
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Abstract

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features. View Full-Text
Keywords: intelligent fault diagnosis; dual-tree complex wavelet transform (DTCWT); convolutional neural network (CNN); pattern recognition intelligent fault diagnosis; dual-tree complex wavelet transform (DTCWT); convolutional neural network (CNN); pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sun, W.; Yao, B.; Zeng, N.; Chen, B.; He, Y.; Cao, X.; He, W. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network. Materials 2017, 10, 790.

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