期刊
2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE MATERIALS PROCESSING AND MANUFACTURING (SMPM 2019)
卷 35, 期 -, 页码 1184-1189出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.promfg.2019.06.075
关键词
Deep belief network; multi-source heterogeneous information fusion; rotor fault diagnosis
类别
资金
- National Natural Science Foundation of China [51505099]
In the age of Internet of Things and Industrial 4.0, new advanced methods need to be proposed to analyse massive multi-source heterogeneous data from rotating machinery since traditional data analysis methods are difficult to mine features effectively and provide accurate fault results automatically. This paper proposes a rotor unbalance fault diagnosis method using deep belief network (DBN) to learn the representative features automatically and accurately identify fault states. Multi-source heterogeneous information composed with vibration signal and shaft orbit plots generated by raw displacement signals can fully exploit multi-sensor information in fault diagnosis. And multi-DBN model was introduced to deal with multi-source heterogeneous information fusion problem containing all fault information which could adaptively learn useful features through multiple nonlinear transformations compared with traditional approaches depending on time-consuming and labour-intensive manual feature extraction. The results indicate that the accuracy of classifying rotor unbalance fault states is up to 100% under proper parameters of DBN which significantly improves the effect of fault recognition and validates effectiveness using the proposed method. (C) 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organizing committee of SMPM 2019.
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