A Blind Source Extraction Method based on Self-learned Dictionary and Its Application in Fault Diagnosis of Bearing Multi-type Fault - Xingguo Cheng; Pu Weng (Jixie chuandong)
When multiple bearings in a gearbox failure simultaneously，conventional blind source extraction （BSE） on the vibration signals of bearing multi-type faults would not be ideal due to the mutual coupling effect among each of the fault sources. A BSE based on sparse representation self-learned dictionary method is proposed to solve the above problem.Firstly，apply the self-learned sparse dictionary originating from sparse representation on the multi-type faults vibration signals directly and a set of self-learning dictionaries are obtained.Then，the multi-type faults vibration signals are re-constructed basing on the obtained learned dictionary to eliminate noise and interference signals.Finally，apply the BSE method on compound fault signals of reconstructed rolling bearings，each single fault signal of rolling bearing is extracted，and then the envelope demodulation analysis is carried out one by one to obtain the corresponding fault characteristics.Feasibility and effectiveness of the proposed method are verified through experiment.