基于小波变换的煤矿机械滚动轴承故障诊断

由于煤矿机械的特殊工作环境,采集到的故障振动信号通常掺杂着非常明显的噪声干扰信号,如何从复杂的信号中提取出有用的信号非常重要。以煤矿机械滚动轴承为研究对象,通过模拟实验并利用小波变换的阈值去噪及分解与重构对煤矿机械滚动轴承振动信号进行分析处理,提取故障特征信息。实验结果表明,与传统机械故障诊断方法相比,该方法在振动信号的有效提取及检测时效性与准确性方面具有明显的优势,既减少了检测人员的工作量,又提高了检测效率。

Fault Diagnosis of Rolling Bearing of Coal Mine Machinery Based on Wavelet Transform

Due to the special working environment of coal mine machinery, the collected fault vibration signals are usually mixed with very obvious noise interference signals. How to extract useful signals from complex signals is very important. Taking coal mine machinery rolling bearings as the research object, through simulation experiments and using wavelet transform threshold denoising, decomposition and reconstruction, the vibration signals of coal mine machinery rolling bearings were analyzed and processed to extract fault feature information. Experiment results show that compared with traditional mechanical fault diagnosis methods, this method has obvious advantages in the effective extraction of vibration signals and detection timeliness and accuracy, which not only reduces the workload of the inspectors, but also improves the detection efficiency.