A Variable Speed Bearing Fault Diagnosis Method Based on BAACMD-IDBO-IMCKD and COT

To address the difficulty of fault feature extraction in rolling bearings, caused by the non-stationarity of vibration signals under variable speed conditions and susceptibility to noise interference, a fault diagnosis method based on BAACMD-IDBO-IMCKD and COT is proposed. Firstly, the signal is decomposed using the Bandwidth-aware Adaptive Chirp Mode Decomposition (BAACMD) algorithm and then reconstructed according to the Gini Index (GI). Secondly, the parameters of the Improved Maximum Correlation Kurtosis Deconvolution (IMCKD) are optimized using the Improved Dung Beetle Optimization (IDBO) algorithm that enhanced with Levy flight and t-distribution strategies. Subsequently, the optimized IMCKD is applied to denoise the reconstructed signal. Finally, the bearing Fault Characteristic Orders (FCO) are obtained after Computed Order Tracking (COT). The results demonstrate that this method effectively reduces noise interference and accurately extracts the FCO of variable speed bearings.