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

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

Blog Article

ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing 100w products 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 berkley power worm 100 pack 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.