Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.Ho...Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.However,effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals.Furthermore,inefficient feature extraction results in substantial time wastage,making it hard to apply in realtime monitoring.A novel feature extraction method for diagnosing bearing faults using multiscale improved envelope spectrum entropy(MIESE)is proposed in this work.First,bearing vibration signals are analyzed across multiple scales,and improved envelope spectrum entropy(IESE)is extracted fromthese signals at each scale to form an original feature set.Subsequently,joint approximate diagonalization eigenmatrices(JADE)is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set.Finally,the newly generated feature set is input into support vectormachines(SVMs)to effectively diagnose bearing health status.Two cases studies are employed to demonstrate the reliability of the proposed method.The results illustrate that the proposed method can improve the stability of extracted features and increase the computational efficiency.展开更多
基金supported in part by the Key Basic Research Project MKF20210008.
文摘Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.However,effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals.Furthermore,inefficient feature extraction results in substantial time wastage,making it hard to apply in realtime monitoring.A novel feature extraction method for diagnosing bearing faults using multiscale improved envelope spectrum entropy(MIESE)is proposed in this work.First,bearing vibration signals are analyzed across multiple scales,and improved envelope spectrum entropy(IESE)is extracted fromthese signals at each scale to form an original feature set.Subsequently,joint approximate diagonalization eigenmatrices(JADE)is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set.Finally,the newly generated feature set is input into support vectormachines(SVMs)to effectively diagnose bearing health status.Two cases studies are employed to demonstrate the reliability of the proposed method.The results illustrate that the proposed method can improve the stability of extracted features and increase the computational efficiency.
