Rolling bearings as an important part of rotating machinery,it operates in harsh environments resulting in non-linear and non-smooth vibration signals,which makes it difficult to distinguish between fault signals and normal signals.In view of this,intelligent fault diagnosis method combining multi-modal mutual dimensionless indicators(MMDI)and broad learning system(BLS)was pro-posed.The optimization complete ensemble empirical mode decomposition with adaptive noise(OCEEMDAN)was used to decompose and preprocess the observable signal of the bearing with wavelet threshold,and the effective intrinsic mode function(IMF)was recon-structed and MDI was extracted to construct the MMDI.The back propagation algorithm and superposition module mode were used to op-timize the BLS,which could quickly identify different types of faults.Finally,the proposed method was verified through the datasets pro-vided by Case Western Reserve University Bearing Data Center and some laboratory,the average accuracy was 99.8％and 100％,re-spectively,the portability and effectiveness of the method were verified.
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