A Novel Multimode Fault Classification Method Based on Deep Learning

Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification...

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Bibliographic Details
Published inJournal of control science and engineering Vol. 2017; no. 2017; pp. 1 - 14
Main Authors Zhou, Funa, Wen, Chenglin, Gao, Yulin
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Artificial intelligence
Automation
Classification
Faults
Feature extraction
Hierarchies
Industrial equipment
Information dissemination
Learning
Machinery
Mathematical models
Neural networks
Partitions
Roller bearings
Signal processing
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Summary:Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.
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ISSN:1687-5249
1687-5257
DOI:10.1155/2017/3583610