Minghang Zhao1, Baoping Tang1, Myeongsu Kang2, Michael G. Pecht2
1Chongqing University, Chongqing, China
2CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
One of the significant tasks in data-driven fault diagnosis methods is to configure a good feature set involving statistical parameters. However, statistical parameters are often incapable of representing the dynamic behavior of planetary gearboxes under variable operating conditions. Although the use of deep learning algorithms to find a good set of features for fault diagnosis has somewhat improved diagnostic performance, the lack of domain knowledge incorporated into deep learning algorithms has limited further improvement. Accordingly, this paper developed a variant of deep residual networks (DRNs), the so-called deep residual networks with dynamically weighted wavelet coefficients (DRN+DWWC) to improve diagnostic performance, which takes a series of sets of wavelet packet coefficients on various frequency bands as an input. Further, the fact that no general consensus has been reached as to which frequency band contains the most intrinsic information about a planetary gearbox's health status calls for “dynamic weighting layers” in the DRN+DWWC and the role of the layers is to dynamically adjust a weight applied to each set of wavelet packet coefficients to find a discriminative set of features that will be further used for planetary gearbox fault diagnosis.
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