Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine

Abstract

ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Subsequently, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis.ResultsResults demonstrate that the proposed method, using only a small amount of bearing diagnostic data, automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s, a final recognition accuracy of 99.29% was achieved. The proposed method reduces diagnostic time and exhibits high diagnostic accuracy, providing a new approach for handling massive bearing data in fault diagnosis.

Keywords

• Compressed sensing
• Bearing
• Kernel function
• Extreme learning machine
• Fault diagnosis