Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

• Published in: Advances in mechanical engineering Vol. 14; no. 1
• Main Authors: Deng, Linfeng, Zhang, Aihua, Zhao, Rongzhen
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2022; Sage Publications Ltd; SAGE Publishing
• Subjects: Bearings; Decomposition; Fault detection; Fault diagnosis; Feature extraction; Identification methods; Kurtosis; Mathematical analysis; Principal components analysis; Roller bearings; Rotating machinery; Support vector machines; Vibration; variational mode decomposition; support vector machine; energy and kurtosis; intelligent identification; principal component analysis; Incipient rolling bearing faults
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/16878140211072990

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.