《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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ISSN 1001-4551

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Zhejiang Machinery and Electrical Group

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Editorial of Journal of Mechanical & Electrical Engineering

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Application of IMIBSE and ISOMAP in fault diagnosis of rotating machinery

Published:2024-06-27 author:ZHOU Jiyan, LIU Jinfeng, HU Yihua. Browse: 38 Check PDF documents

Application of IMIBSE and ISOMAP in fault diagnosis of rotating machinery

ZHOU Jiyan1, LIU Jinfeng1, HU Yihua2

(1.College of Robotics, Guangdong Provincial Science and Technology Cadre College, Zhuhai 519000, China;

2.The Institute of

International Education, Guangxi University of Science and Technology, Liuzhou 545006, China)

Abstract: Aiming at the problem that the region division standard of basic entropy method was not accurate, which could not effectively measure the complexity of vibration signal of rotating machinery, and the accuracy of rotating machinery fault diagnosis was poor, a rotating machinery fault diagnosis method based on improved multi-scale improved basic entropy (IMIBSE), isometric feature mapping (ISOMAP) and random forest (RF) was proposed. Firstly, the regional division criterion of variance was used to improve the basic entropy, and combining with the improved coarse-grained processing, the IMIBSE method was proposed and used to extract the fault characteristics of rotating machinery. Then, ISOMAP method was used to reduce the feature dimension of the original fault features, and a group of features that contribute the most to classification was selected as the fault sensitive features. Finally, a multi-fault classifier was built based on RF, and the fault sensitive features were input to RF model for training and testing, so as to realize the fault identification of rotating machinery. The IMIBSE method was compared with composite multi-scale basic entropy, multi-scale improved basic entropy and multi-scale basic entropy by using two fault data sets of rolling bearing and centrifugal pump. The experimental results show that IMIBSE method not only have the best visualization effect, but also have the highest recognition accuracy, both reaching 100%, and the average classification accuracy of each is 100% and 99.8%, respectively. Comparing with other fault diagnosis methods, IMIBSE method has higher accuracy and is suitable for small sample fault identification.

Key words: gear box; centrifugal pump; fault diagnosis; improved multi-scale improved basic entropy (IMIBSE); isometric feature mapping (ISOMAP); random forest (RF); improved coarse-grained processing

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