基于FIR分解的轴承故障快速诊断方法研究

Research on Fast Fault Diagnosis Method of Bearing Based on FIR Decomposition

为实现轴承故障的快速准确诊断,以互相关和互信息为基础构造一种针对轴承的快速故障诊断方法。该方法首先运用有限长单位冲激响应(Finite Impulse Response,简称FIR)滤波器对各单一故障(包括内圈、外圈、滚珠、保持架)振动信号进行分解,降低信号分解过程中因模态混叠造成的干扰,以力学分析建立的各故障振动模型为参考,对分解后的子信号采用互相关分析法,选出表征故障特征的子信号,计算子信号透露的信息量——互信息,用于构造故障特征矩阵,最后由K最近邻分类算法(K-Nearest Neighbor,简称KNN算法)的识别结果验证该算法对实现轴承故障快速识别具有优势。

In order to realize the fault diagnosis of bearing fast and accurately,a fast fault diagnosis method for bearing was constructed based on cross correlation and mutual information.In this method,each single fault vibration signal(including inner ring,outer ring,ball and cage)was decomposed by Finite Impulse Response(FIR)which reduce aliasing caused by interference in the process of signal decomposition.Taking the fault vibration model established by mechanics analysis as the reference and calculated the correlation coefficient,the sub-signal that characterizes the fault feature was selected.Calculated the mutual information of sub-signal and constructed the fault feature matrix.Finally,the recognition results of K-Nearest Neighbor(KNN)is used to validate that the algorithm has the advantage in rapid identification of bearing failure.