基于声振信号的电机故障诊断方法

Motor Fault Diagnosis Method Based on Acoustic Vibration Signal

电机在工作时产生的异常噪音往往表明电机存在潜在故障或处于不良的工作状态.本研究提出了一种基于振动信号分析的电机异音检测方法,该方法通过安装加速度传感器和麦克风等设备采集电机运行时的振动信号数据,并对采集到的振动信号进行信号处理和特征提取.接着构建一个分类模型,利用支持向量机算法对提取的特征进行训练和分类.实验结果表明,该方法在检测电机异常噪音方面展现出良好的性能和准确性.大量实际电机运行数据测试结果表明该方法能够有效地判断电机是否存在异常噪音,并提前预测潜在故障.

Abnormal noises generated by motors during normal operation often indicate potential faults or suboptimal 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.