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Status Accepted
journal_subject Part A
Article Type Regular Paper (More than 4 pages)
Article Filed Maintenance Engineering
Article Title Signal Fusion and Hybrid Principal Component Analysis Based Automatic Diagnosis Method for Structure Abnormal of Rotating Machinery
Keywords Signal fusion, Least squares projection, hybrid principal component analysis, Fault diagnosis
Abstract The fault diagnosis of the equipment can ensure the smooth operation of the equipment. Especially, the structural abnormality of the equipment will affect the stability and reliability of the equipment. In this paper, a new method of signal fusion is proposed, which fuses the symptom parameters of multi-source vibration signals. The optimal data fusion model can effectively improve the reliability of data fusion algorithm, and sensitive symptom parameters are selected by the identification index (DI). In addition, a hybrid principal component analysis method (HPCA) is proposed. The least squares projection method is used to pre-process sensitive symptom parameters, which can effectively enhance the correlation of the symptom parameters. To recognize the fault type exactly and realize visualization, the principal component analysis algorithm is applied to reduce the dimension of the pre-processed symptom parameters, and precise diagnosis is achieved by analyzing the Mahalanobis distance of the integrated parameter. The experimental results show that the combination of signal fusion and hybrid principal component analysis can accurately identify the fault types.
Authors
First Name Middle Name Last Name E-Mail Corresponding
Shi Li 550705091@qq.com No
Kotaro Taki No
Ke Li like@jiangnan.edu.cn No
HongTao Xue xueht@ujs.edu.cn No
Peng Chen chen@bio.mie-u.ac.jp Yes
Liuyang Song xq_0703@163.com Yes
Huaqing Wang hqwang@mail.buct.edu.cn No
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