滚动轴承复合故障的混合协同诊断方法

针对传统复合故障诊断方法存在故障难以完全分离的缺点, 提出了滚动轴承复合故障的混合协同诊断方法。首先对观测信号的协方差矩阵进行奇异值分解, 求出白化矩阵并对复合故障信号进行白化处理; 然后, 利用联合对角化方法对白化后的故障矩阵进行对角化变形, 通过最小化对角化程度函数得到正交矩阵; 最后, 通过正交矩阵估计故障源信号矩阵, 实现复合故障的分离; 由于二阶盲辨识方法分离出的故障信号间存在无序性以及相似性, 导致分离信号故障类型难以确定, 因此将分离后的故障信号进行短时傅里叶变换, 通过分离信号的时频谱图与原信号时频谱图进行比较, 并根据趋势一致性确认所对应的故障类型。最终, 以广东省石化装备故障诊断重点实验室的轴承数据进行实验论证, 结果表明, 二阶盲辨识协同短时傅里叶变换能有效将滚动轴承的复合故障信号分离出来, 工程上具备可操作性和极大的应用价值。

Hybrid Collaborative Diagnosis Method for Rolling Bearing Composite Faults

Traditional composite faults diagnosis approaches applied in isolating rolling bearing mixed faults are not effectively.To overcome this defect, this paper proposes a hybrid collaborative diagnosis method for rolling bearing composite faults.Firstly, by using second order blind identification method, we calculate the whiten matrix of fault signals.Then, by minimizing the degree of diagonalization function, we can realize joint diagonalization on whiten fault matrix.Finally, we use singular value decomposition for the orthogonal matrix, further we can estimate the signal matrix of fault sources from the compound fault signals.But through the above procedure, we can only get the out-of-order signals.If there are some similar signals, it is hard to recognize them by our intuitions.So we next apply the short-term Fourier transform to solve this problem, by comparing the time-frequency spectrograms trends between isolated and original signals, we can clearly identify the fault types.At last, to validate its effectiveness, we put the fault data from the Key Laboratory of Fault Diagnosis of Petrochemical Equipment in Guangdong Province into this proposed method, the results show its practical value in mechanical engineering.