基于改进小波阈值-SVM的齿轮故障信号识别

Gear Fault Signal Recognition Based on Improved Wavelet Threshold-SVM

针对在齿轮疲劳试验中需要多次停机拍照的问题,提出基于改进的小波域阈值降噪方法,对采集的齿轮啮合的声发射信号分析,并提取特征矢量作为支持向量机的输入特征向量,识别出故障信号,根据齿轮旋转周期,确定缺陷轮齿。与开箱拍照的记录进行对比,计算得出的问题轮齿和照片记录吻合。该方法减少了因停机拍照造成的齿轮工作数据采集不准确的问题,同时也减少了工作人员的工作量,在后续的实验中该方法得到了有效的应用。

Aiming at the problem that the gear needed to be stopped for many times to take pictures in gear fatigue test,an improved wavelet domain threshold denoising method was proposed,the acoustic emission signal of the meshing gear was analyzed and processed,and the feature vector was extracted as the input feature vector of the support vector machine,and the identified fault signal was used to determine the defective gear teeth according to the gear rotation period.Compared with the records of photos taken after unpacking,defective gear teeth which were calculated finally coincided with the photo records.The method reduces the influence of inaccurate data collection of gears caused by shutdown and taking pictures,and also reduces the workload of workers,and the method has been effectively applied in subsequent experiments.