IEEE Transactions on Industrial Electronics, August, 2018, doi: 10.1109/TIE.2018.2866050

Multi-wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Minghang Zhao1, Myeongsu Kang2, Maoping Tang2, and Michael Pecht1
1State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044
2CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


Wavelet transform, an effective tool to decompose signals into a series of frequency bands, has been widely used for vibration-based fault diagnosis in machinery. Likewise, the use of deep learning algorithms is becoming popular to automatically learn discriminative features from input data for the sake of improving diagnostic performance. However, the fact that no general consensus has been reached as to which wavelet basis functions are useful for diagnosis motivated this investigation of methods to fuse multi-wavelet transforms into deep learning algorithms. In this paper, two methods—i.e., multi-wavelet coefficients fusion in deep residual networks by concatenation (MWCF-DRN-C) and multi-wavelet coefficients fusion in deep residual networks by maximization (MWCF-DRN-M)—were developed to capture discriminative information from diverse sets of wavelet coefficients for fault diagnosis. The efficacy of the developed methods was verified by applying them to planetary gearbox fault diagnosis.

This article is available online here and to CALCE Consortium Members for personal review.

© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

[Home Page] [Articles Page]
Copyright © 2018 by CALCE and the University of Maryland, All Rights Reserved