A compound fault diagnosis method based on health state feature decoupling

In modern intelligent manufacturing systems, fault diagnosis is of great significance to ensure the safe operation of equipment. As the complexity of equipment increases, various components are tightly coupled, leading to occurrence of compound faults either between components or within the same component. This study proposes a compound fault diagnosis method based on health state feature decoupling. Firstly, the input signals are decomposed into normal and single fault-related components in the feature space. Meanwhile, the distance between different health state components of the signal is maximized by orthogonality constraints to enhance the independence of each other. Then, the feature decoupling is achieved by minimizing the distribution discrepancy between the same health state features of signals from different categories. During the decoupling process, signal reconstruction based on the sum of feature components is utilized to ensure the decoupled features are meaningful. Finally, a classifier is used to achieve accurate compound fault diagnosis. Experimental results verify the superiority of the proposed method on compound fault diagnosis.