Application of resonance method in composite fault diagnosis of bearings

Poisson white noise is a more accurate representation than ideal Gaussian white noise for simulating the noise background during early bearing failures. In the context of strong Poisson white noise interference, the failure mode of rolling bearings usually remains undetermined. This paper proposes a new method for diagnosing unknown bearing faults in bearings, diagnosis is proposed, which effectively identifies unknown faults across various components. The theoretical characteristics of Poisson white noise are initially analyzed and discussed. Then, to extract potential fault characteristic information, a false characteristic frequency of bearing is constructed in the response spectrum, and the response results at this false fault characteristic are obtained. Finally, coherent resonance (CR) is introduced, and false frequencies caused by false faults are eliminated by defining a quality factor. To verify the effectiveness of the method, the experimental and simulation results were compared with the decomposition results of the SVMD algorithm. The SNR of the experimental signals for outer and inner ring faults under variable speed conditions increased to 8.62[Formula: see text]dB and 11.74[Formula: see text]dB, respectively. Results indicate show that this method not only successfully identifies fault features, but also exhibits a strong noise reduction effect.