改进TSA降噪与平方包络谱分析的故障特征提取

Fault feature extraction based on improved TSA denoising and squared envelope spectrum

为实现旋转机械设备故障诊断,提出基于相关性检测的振动信号改进时域同步平均(Time Synchronous Averaging,TSA)降噪方法,并利用平方包络谱提取故障特征。在采样振动信号中任取一段参考子信号,令参考子信号从振动信号初始时间开始,沿时间轴逐点滑动至终了时间,同时计算参考子信号与其在振动信号中遮掩子信号之间的相关系数;利用步长迭代算法获取相关系数最优阈值,选择与参考子信号相关系数大于最优阈值的所有遮掩子信号作为总体同步信号,平均计算后获得降噪的目标信号;对目标信号进行平方包络谱分析。仿真信号、齿轮和轴承振动信号的分析结果表明,所提出的方法能够有效用于旋转机械设备的振动信号降噪与故障特征提取。

In order to achieve the fault diagnosis of the rotating machine equipment,a method based on improved time synchronous averaging(TSA)denoising using correlation detection and fault features extracting using squared envelope spectrum is proposed.A reference sub-signal is arbitrarily taken from the sampled vibration signal.The reference sub-signal starts from the initial time of the vibration signal and moves point by point along the time axis to the end time.Meanwhile,the correlation coefficient between the reference sub-signal and its masking sub-signal in the vibration signal is calculated.The correlation coefficient optimal threshold are obtained using the step iterative algorithm.All the masking sub-signals whose correlation coefficients are greater than the optimal threshold are selected as overall synchronous signals whose arithmetic average is calculated for the denoised target signal.The target signal analysis is achieved using squared envelope spectrum.The analysis results of the simulated signal,gear and rolling bearing fault vibration signals have shown that the proposed method is an efficient implementation for rotating machine vibration signal denoising and fault feature extraction.