Fault Diagnosis of Rotating Machinery Based on CHK‐Optimized MOMEDA

Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) leverages its unique multiobjective optimization capability to effectively enhance specific fault‐related features in signals. However, its core performance relies on the accurate prior definition of the fault period T . This inherent dependence on precise prior configuration limits its practical application. Moreover, the fitness function of the existing optimization algorithm is only designed for a specific single rotating component in the process of MOMEDA’s parameter selection. Therefore, an improved MOMEDA method based on a new fitness function is proposed. This approach begins with the design of a comprehensive fitness function, CHK, that integrates both impulsive and periodic characteristics. A dynamic weighting mechanism adaptively balances the fault features of diverse targets, enabling effective identification of various fault patterns, including those in bearings and gears. Furthermore, a particle swarm optimization (PSO) algorithm is employed to adaptively optimize the deconvolution period T , a critical parameter in MOMEDA. This optimization algorithm employs the proposed CHK indicator as the fitness function. The effectiveness and generalization of the proposed method are validated through experiments of bearing and gear fault diagnosis. Finally, the experimental results demonstrate that the proposed method is able to accurately extract subtle fault features from different rotating machinery. It also shows that this method exhibits significant advantages regarding anti‐interference capabilities and application scope, when compared with SK‐based MOMEDA, envelope entropy‐based MOMEDA, and peak factor of envelope spectrum‐based MOMEDA.