Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

• Published in: Journal of low frequency noise, vibration, and active control Vol. 36; no. 4; pp. 354 - 365
• Main Authors: Dong, Shaojiang, Luo, Tianhong, Zhong, Li, Chen, Lili, Xu, Xiangyang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2017; Sage Publications Ltd
• Subjects: Algorithms; Decomposition; Fault detection; Fault diagnosis; Kernels; Particle swarm optimization; Principal components analysis; fault diagnosis; k-nearest neighbour; particle swarm optimization; kernel principal component analysis; Bearing
• ISSN: 1461-3484; 2048-4046
• DOI: 10.1177/1461348417744302

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.