Open AccessIntelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM
Author(s) -
Liya Deng,
Aihua Zhang,
Rongzhen Zhao
Publication year - 2022
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/16878140211072990
Subject(s) - bearing (navigation) , kurtosis , support vector machine , fault (geology) , vibration , feature extraction , pattern recognition (psychology) , principal component analysis , artificial intelligence , engineering , energy (signal processing) , computation , computer science , hilbert–huang transform , control theory (sociology) , algorithm , mathematics , acoustics , statistics , physics , control (management) , seismology , geology
Rolling bearings are the key components of rotating machinery. Incipient fault diagnosis of bearing plays an increasingly important role in guaranteeing normal and safe operation of rotating machinery. However, because of the high complexity of the fault feature extraction, the incipient faults of rolling bearings are difficult to diagnose. To solve this problem, this paper presents a new incipient fault intelligent identification method of rolling bearings based on variational mode decomposition (VMD), principal component analysis (PCA), and support vector machines (SVM). In the proposed method, the bearing vibration signals are decomposed by using VMD, and a series of intrinsic mode functions (IMFs) with different frequencies are obtained. Then, the energy and kurtosis values of each IMF are calculated to reveal the intrinsic characteristics of the vibration signals in different scales. Finally, all energy and kurtosis values of IMFs are processed via PCA and subsequently fed into SVM to achieve the bearing fault identification automatically. The effectiveness of this method is verified through the experimental bearing data. The verification results indicate that the proposed method can effectively extract the bearing fault features and accurately identify the bearing incipient faults, and outperform the two compared methods obviously in identification accuracy and computation time.