Sparse Deep Stacking Network for Fault Diagnosis of Motor

A sparse deep learning method is proposed to overcome overfitting risk of deep networks with a large number of nodes and layers. Deep stacking network (DSN) is a classic and effective deep learning method, and its sparse form is presented to generate the sparse deep learning method. In DSN, output l...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2018-07, Vol.14 (7), p.3261-3270
Main Authors: Sun, Chuang, Ma, Meng, Zhao, Zhibin, Chen, Xuefeng
Format: Article
Language:English
Subjects:
Coding
Deep learning
Deep stacking network (DSN)
Electric motors
Fault diagnosis
Feature extraction
kernel method
Labels
Machine learning
Mechanical engineering
motor fault diagnosis
Motors
Neural nets
Regularization
sparse deep learning
sparse regularization
Sparsity
Stacking
Teaching methods
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Summary:A sparse deep learning method is proposed to overcome overfitting risk of deep networks with a large number of nodes and layers. Deep stacking network (DSN) is a classic and effective deep learning method, and its sparse form is presented to generate the sparse deep learning method. In DSN, output labels are encoded as a series consisted of 1 and 0. This coding strategy makes output labels to be sparse. However, sparsity of output labels is not considered in DSN model. Considering this limitation, sparse DSN (SDSN) is developed in this paper. The SDSN extends tradition DSN in sparsity characterization using a sparse regularization term. By this term, predicted output label is constrained to be similar with ideal output label that is binary and consisted of continuous 1 and 0 with a sidestep shape. The sparse regularization term is used as a soft threshold strategy to set irrelevant element to be zero, by which effectiveness of SDSN is enhanced. Case studies about fault diagnosis of motor are used to validate performance of SDSN. Comparison between SDSN and commonly used deep networks is further conducted. The results show advance of SDSN for fault classification.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2819674