To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ense...To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the Improved Maximum Correlated Kurtosis Deconvolution(IMCKD).Utilizing the kurtosis criterion,the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD,which significantly reduces noise in the measured signal.This approach maximizes the energy amplitude at the fault characteristic frequency,facilitating fault feature identification.Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components.Compared with traditional methods,it significantly improves the signal-to-noise ratio and more accurately identifies fault features,meeting the requirements for discriminating rolling bearing faults.The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high-speed wire department of a Long Products Mill.It successfully extracted weak characteristic information of early bearing faults,achieving the expected diagnostic results.This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications,demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.展开更多
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
基金Inner Mongolia Autonomous Region Science and Technology Major Special Project,Grant/Award Number:2021ZD0019-4。
文摘To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the Improved Maximum Correlated Kurtosis Deconvolution(IMCKD).Utilizing the kurtosis criterion,the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD,which significantly reduces noise in the measured signal.This approach maximizes the energy amplitude at the fault characteristic frequency,facilitating fault feature identification.Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components.Compared with traditional methods,it significantly improves the signal-to-noise ratio and more accurately identifies fault features,meeting the requirements for discriminating rolling bearing faults.The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high-speed wire department of a Long Products Mill.It successfully extracted weak characteristic information of early bearing faults,achieving the expected diagnostic results.This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications,demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.
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.
