摘要
In field of rolling bearing fault diagnosis, the sampled bearing vibration signals will be generally disturbed with noise. In noisy environment, the conventional blind source separation method is not good for diagnosing bearing faults. In this paper, wavelet de-noising method and blind source separation technology have been combined. In order to achieve fault diagnosis of rolling bearing, firstly wavelet soft threshold de-noising method has been applied on sampled signals. Then the better robust JADE algorithm has been applied in signals blind source separation. At last, vibration signals bearing inner and outer faults of have been analyzed in this paper, and the corresponding bearing faults have been diagnosed successfully. The proposed research methods provide a new way for diagnosing rolling bearing＇s mixed faults under noise
In field of rolling bearing fault diagnosis, the sampled bearing vibration signals will be generally disturbed with noise. In noisy environment, the conventional blind source separation method is not good for diagnosing bearing faults. In this paper, wavelet de-noising method and blind source separation technology have been combined. In order to achieve fault diagnosis of rolling bearing, firstly wavelet soft threshold de-noising method has been applied on sampled signals. Then the better robust JADE algorithm has been applied in signals blind source separation. At last, vibration signals bearing inner and outer faults of have been analyzed in this paper, and the corresponding bearing faults have been diagnosed successfully. The proposed research methods provide a new way for diagnosing rolling bearing＇s mixed faults under noise
