Motor Fault Diagnosis Method Based on Acoustic Vibration Signal
Abnormal noises generated by motors during normal operation often indicate potential faults or sub-optimal working conditions. In this study, we propose a method for detecting abnormal motor noise based on vibration signal analysis. The method involves collecting vibration signal data from the motor during operation through the introduction of devices such as acceleration sensors and microphones. The acquired vibration signals undergo signal processing and feature extraction. Subsequently, a classification model is constructed, where the extracted features are trained and classified by using the support vector machine algorithm. The experimental results demonstrate that the proposed method for detecting motor abnormal noise exhibits excellent performance and accuracy. Through extensive testing on actual motor operation data, this method effectively identifies the presence of abnormal noises in the motor and enables early prediction of potential faults.
anomalous motor noisefeature extractionsupport vector machine algorithm