Bearing fault diagnosis based on speed signal and CNN model

In the fault diagnosis of the existing permanent magnet synchronous motor, the characteristics of the fault are often extracted based on the vibration acceleration signal. The acquisition of vibration acceleration requires the additional installation of expensive sensors, and the diagnosis effect is greatly affected by the surrounding environment. On the other hand, at this time, due to the influence of the load side, the fault characteristics of the bearing and other faults are easily submerged, resulting in a diagnosis failure or even a misdiagnosis. This paper proposes a bearing fault diagnosis method based on the motor speed signal. Combined with the CNN, the feature extraction and analysis of the rotational speed signal are carried out, and an improved algorithm is proposed by combining the artificially selected eigenvalues in the frequency domain. The experimental results show that this method can still complete the diagnosis of bearing faults well in the presence of misalignment fault interference, which shows the potential of deep learning technology represented by CNN in the use of rotational speed signals to diagnose various types of motor faults and provide experimental and theoretical basis for it.