《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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A comprehensive fault detection and pattern recognition model for rotating machinery

Published:2024-08-30 author:CAO Lifang, YUAN Zheng, YIN Jiu, et al. Browse: 581 Check PDF documents

A comprehensive fault detection and pattern recognition

model for rotating machinery

CAO Lifang1, YUAN Zheng1, YIN Jiu2, GUO Haitao3

(1.Yellow River Conservancy Technical Institute,Kaifeng 475004, China; 2.Department of Mechanical and Electrical

Engineering, Hubei Light Industry Technology Institute, Wuhan 430070, China; 3.School of Civil Engineering and

Transportation, South China University of Technology, Guangzhou 510640, China)

Abstract: Aiming at the problem that the traditional fault diagnosis methods only focus on the fault detection part and there are few studies on whether there are faults in the sample. A comprehensive fault diagnosis model for rotating machinery based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-attention entropy (AE) and slime mould algorithm optimized extreme learning machine (SMA-ELM) was proposed. Firstly, according to the complexity difference between normal samples and fault samples, the attention entropy threshold was established. The AE value of the rotating machine was calculated and compared with the threshold value. If the entropy value was less than the threshold value, it was indicated that the sample had a fault, otherwise, the sample was healthy. Then, CEEMDAN was used to decompose the vibration signal of the fault sample, and extract the AE of the first 6 order components. Finally, the fault features were input into the SMAELM model for fault identification. The reliability of the comprehensive fault diagnosis model was studied using three rotating machinery fault datasets. The research results show that the threshold setting method can detect whether there is a fault in the sample with 100% accuracy, and the subsequent fault diagnosis model can accurately detect the fault type of the sample with the recognition accuracy of 99.44%, 100% and 98%, respectively. This comprehensive fault diagnosis model can prevent normal samples from being misjudged as faulty samples, providing a feasible approach for fault detection of rotating machinery.

Key words: rotating machinery; rolling bearing comprehensive fault diagnosis; fault threshold; attention entropy(AE); complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN); slime mould algorithm optimized extreme learning machine (SMAELM)

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