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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
Abstract: The traditional multi-scale feature extraction method based on coarse-based processing could not consider the fault information of the high frequency part of the signal, which made it difficult to accurately characterize the fault state and dynamic characteristics of the rolling bearing, resulting in the reliability and accuracy of fault diagnosis. To solve this problem, a fault diagnosis method of rolling bearing based on improved hierarchical slope entropy (IHSloE) and random forest (RF) was proposed. Firstly, improved hierarchical processing was used instead of coarse-grained processing to achieve multi-scale signal analysis, and based on slope entropy, a nonlinear dynamic index called improved hierarchical slope entropy was proposed. Then, IHSloE method was used to extract the fault characteristics of the rolling bearing vibration signal, and the fault characteristics of the rolling bearing were established. Finally, a multi-fault classifier was established based on RF model, and the fault characteristics were input to the RF classifier for training and testing, so as to realize fault identification of rolling bearings. The rolling bearing data sets were used for experiments and compared with other fault feature extraction indexes. The research results show that IHSloE method can quickly and effectively extract high-frequency fault features from vibration signals by using the improved hierarchical processing, and the diagnostic accuracy reaches 99%, while the feature extraction time is only 149.35 s. Comparing with the feature extraction methods using coarse-grained processing and hierarchical processing, the accuracy is at least 2% and 1% higher, respectively, which can be applied to the fault diagnosis of rolling bearings.
Key words: high frequency part characteristic of fault signal; improved hierarchical slope entropy(IHSloE); random forest(RF) classifier; multiscale feature extraction method; improved hierarchical processing; reliability of fault diagnosis
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- Chinese Core Periodicals
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- Chinese Sci-tech Core Periodicals
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- SA, INSPEC Indexed
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- CSA: T Indexed
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- UPD:Indexed
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2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
