A novel hybrid method based on KELM with SAPSO for fault diagnosis of rolling bearing under variable operating conditions. (June 2021)

• Record Type: Journal Article
• Authors: Su, Hao; Xiang, Ling; Hu, Aijun; Gao, Benfeng; Yang, Xin
• Is Part Of: Measurement. Volume 177(2021)
• Publication Date: 2021-06
• Subjects: Fault diagnosis -- Kernel-based extreme learning machine -- Simulated annealing particle swarm optimization -- Feature selection -- Isomap; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109276
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel method is proposed for fault diagnosis of bearings under various working conditions. SAPSO is added to optimize the parameters of KELM. The faults of rolling bearing are detected effectively. Abstract: It's a great challenge to detect fault types from the measured vibration signals automatically and efficiently under variable running condition. A novel hybrid method based on simulated annealing particle swarm optimization (SAPSO) and improved kernel-based extreme learning machine (IKELM) is proposed for diagnosing rolling bearing faults under variable conditions in the paper. Multi-domain fault features from time domain, frequency domain and time–frequency domain are extracted and combined to reconstruct a high-dimensional feature collection by applying VMD, and isometric feature mapping is adopted to lower the dimension of the high-dimensional feature collection. The parameters of KELM are optimized to adapt for the fault feature identification by using SAPSO method. The proposed SAPSO-IKELM can be applied to diagnose the fault automatically and accurately from the two rolling bearing experiments. The results indicate SAPSO-IKELM could remove dependence on artificial feature selection and reach high classification accuracy under varying conditions.