To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposi...To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition(ACMD)and an optimized Maximum Correlation Kurtosis Deconvolution(MCKD)using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation(SCSSA).Firstly,ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions(IMFs).Then,the IMFs are filtered according to the Gini coefficient indicator,with the IMF having the largest Gini coefficient selected as the optimal component.Secondly,the SCSSA is employed to iteratively optimize the filter length L,fault signal period T,and displacement parameter M in the MCKD algorithm,determining the optimal parameter combination for MCKD.This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis.The optimized MCKD is then applied to the optimal component,and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum.To verify the effectiveness and generalizability of the proposed method,simulations,experimental signals from the Case Western Reserve University Bearing Center,and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults.The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.展开更多
文摘To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions,this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition(ACMD)and an optimized Maximum Correlation Kurtosis Deconvolution(MCKD)using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation(SCSSA).Firstly,ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions(IMFs).Then,the IMFs are filtered according to the Gini coefficient indicator,with the IMF having the largest Gini coefficient selected as the optimal component.Secondly,the SCSSA is employed to iteratively optimize the filter length L,fault signal period T,and displacement parameter M in the MCKD algorithm,determining the optimal parameter combination for MCKD.This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis.The optimized MCKD is then applied to the optimal component,and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum.To verify the effectiveness and generalizability of the proposed method,simulations,experimental signals from the Case Western Reserve University Bearing Center,and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults.The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.
