The operating environment of the high-pressure grinding roll is complicated， and the signal is easily polluted by noise. Traditional algorithms find it difficult to extract the fault characteristics of high-pressure grinding rolls effectively and select the parameters of the stochastic resonance system. To address these issues， an operation fault diagnosis method of high-pressure grinding roll based on adaptive reconstruction features with parameter optimization was proposed. First， the ensemble empirical mode decomposition （EEMD） method was employed to decompose the high-pressure grinding roll’s vibration signal into several intrinsic mode function （IMF） components. Secondly， the mixed criterion of correlation coefficient and mutual information was used to adaptively screen the component signals with the strongest abnormal operation characteristics and reconstruct them. Then， the salp swarm algorithm （SSA） was introduced to build the adaptive stochastic resonance （SR） parameter optimization mechanism by combining the population probabilistic mutation mechanism. Finally， an operation fault diagnosis algorithm of high-pressure grinding roll based on an adaptively selected component reconstruction signal was proposed. Simulation results verify the effectiveness of the proposed method.
