Step-by-Step Compound Faults Diagnosis Method for Equipment Based on Majorization-Minimization and Constraint SCA

Compound faults often occur simultaneously or successively due to the complexity of intelligent mechatronic systems. The generation of such group faults will bring more difficulties to fault diagnosis. To separate the compound fault under the complex condition and improve the accuracy of the separated signal, a step-by-step compound faults diagnosis method for equipment based on majorization-minimization (MM) and constraint sparse component analysis (SCA) is proposed in this article. The method can perform under the condition that the measurements are not enough and signal sparsity is insufficient. The proposed SCA framework is the main technique to achieve compound faults separation and it is divided into three steps in this case. In the first step, MM is used to achieve sparse representation of vibration signal to satisfy the prerequisites for SCA and obtained content clustering for matrix estimation. In the second step, expanded potential function is utilized to estimate matrix, which can take advantage of sparse information from mixtures. In the final step, constraint based on the adaptive Laplace dictionary is introduced to obtain the precise source signal. Results of bearing vibration analysis by simulation, experiment, and comparison are presented to illustrate the proposed technique.