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Application of RCMNAAPE in fault diagnosis of rotating machinery
Published:2024-06-27 author:CHU Xiangdong, DAI Lijun, TU Jinzhou, et al. Browse: 500 Check PDF documents
Application of RCMNAAPE in fault diagnosis of rotating machinery


CHU Xiangdong1, DAI Lijun1, TU Jinzhou1, LUO Zhenhuan1, YU Zhen2, QIN Lei3


(1.Huaiyin Cigarette Factory, Jiangsu Zhongyan Industry Co., Ltd., Huai'an 223002, China; 2.Yizhong (Qingdao) Tobacco 

Machinery Co., Ltd., Qingdao 266000, China; 3.School of Mechanical and Electrical Engineering, Wuhan University of 

Technology, Wuhan 430070, China)


Abstract:  Aiming at the defect that refined composite multiscale permutation entropy (RCMPE) could not fully extract fault information from vibration signals of rotating machinery, which led to unstable fault identification accuracy of rotating machinery, a fault diagnosis method for rotating machinery based on refined composite multiscale normalized amplitude aware permutation entropy (RCMNAAPE), Laplace scores (LS) and grey wolf algorithm optimization support vector machine (GWO-SVM) was proposed. Firstly, the amplitude aware permutation entropy was used to replace the permutation entropy in RCMPE, and the RCMNAAPE was proposed to extract the fault characteristics of the vibration signals of rotating machinery and generate the feature samples. Subsequently, LS was used to select fewer features from the original high-dimensional fault feature vectors that can more accurately describe the fault state, and sensitive feature samples were constructed. Finally, the low-dimensional fault feature vector was input into the support vector machine optimized by grey wolf algorithm for training and testing, and the fault identification and classification of rotating machinery samples were completed. The RCMNAAPE-LS-GWO-SVM and other fault diagnosis methods were compared and evaluated by using rolling bearing and gearbox fault data set. The results show that the RCMNAAPE-LS-GWO-SVM fault diagnosis method can effectively identify various kinds of rotating machinery faults, and its recognition accuracy is higher than other fault diagnosis methods, among which the rolling bearing fault recognition accuracy reaches 99.33%, and the identification accuracy of gearbox fault reaches 98.67%. However, the feature extraction efficiency of this method is not good, and the average feature extraction time is respectively 153.02 s and 163.98 s, which is only better than refined composite multiscale fuzzy entropy (RCMFE), but its comprehensive performance is better.

Key words: fault identification accuracy; rolling bearing; gearbox; refined composite multiscale normalized amplitude aware permutation entropy(RCMNAAPE); Laplace scores (LS); grey wolf algorithm optimization support vector machine (GWO-SVM)
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