局部分层非负张量分解算法及在机械故障诊断中的应用(英文)

Local hierarchical non-negative tensor factorization and its application in machinery fault diagnosis

针对传统非负张量分解收敛速度慢,分解精度低的难题,结合three semi-NMF模型,将局部目标函数理论应用于非负张量分解中,提出了基于局部分层的非负张量分解算法,并通过人脸特征提取实验验证了算法的有效性.通过对由空压机不同故障振动信号的双谱构成的张量按该算法分解,得到反映故障特征的基图像及与基图像对应的权值矩阵,建立了特征与故障频率之间的对应关系,并将权值矩阵输入到BP神经网络中对故障进行分类.同时将该方法与其他特征提取方法相比较,实验结果表明该方法有效地提高了空压机故障诊断精度.

Aiming at the slow convergence and low accuracy problems of the traditional non-negative tensor factorization, a local hierarchical non-negative tensor factorization method is proposed by applying the local objective function theory to non- negative tensor factorization and combining the three semi-non- negative matrix factorization（NMF） model. The effectiveness of the method is verified by the facial feature extraction experiment. Through the decomposition of a series of an air compressor＇s vibration signals composed in the form of a bispectrum by this new method, the basis images representing the fault features and corresponding weight matrices are obtained. Then the relationships between characteristics and faults are analyzed and the fault types are classified by importing the weight matrices into the BP neural network. Experimental results show that the accuracy of fault diagnosis is improved by this new method compared with other feature extraction methods.