This article proposes a diesel engine fault diagnosis method that combines Variational Mode Decomposition (VMD) and Extreme Learning Machine (ELM) to diagnose and classify diesel engine faults. Aiming at the nonlinear and non-stationary characteristics of diesel engine vibration signals,an optimized VMD decomposition method based on Sparrow Search Algorithm (SSA) is proposed to achieve good decomposition performance. A classification model based on Grey Wolf Optimization (GWO) algorithm for optimizing ELM is proposed to address diverse types of fault signals in diesel engines,making the classification performance more stable. Finally,the proposed method is applied to the fault detection and recognition of Isuzu 6BB1 diesel engine,with a fault recognition accuracy of 98.04％. The diagnostic results verify that GWO-ELM has high accuracy,and this method is feasible and effective.
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