Fault diagnosis of motor bearing equipment based on vibration signals

In the fault diagnosis technology of electric motors, the vibration signals during operation contain a large number valuable information. The identification of vibration signals can reflect the operating condition of a motor, thereby helping to repair faulty components. The purpose of this article is to establish a vibration-based engine-ball failure diagnostic device. Click in the dynamic input prompt perform default restrictions and given soft threshold denoising on the collected signals, and the denoising effects of the two methods will be compared. Then, signal processing is completed through the analysis method of time-domain feature extraction, which facilitates obtaining effective target feature data, reducing data dimensionality, and facilitating recognition. Finally, these feature parameters were put into the BP artificial neural network for training, and the results showed that the neural network achieved accurate recognition of different types of bearing vibration signals and completed online diagnosis of bearing faults.