Based on field wave data, an empirical formula of wave envelope spectrum is given in this paper. Then the methods of both numerical and physical simulation of sea wave groups with the given spectrum and groupiness par...Based on field wave data, an empirical formula of wave envelope spectrum is given in this paper. Then the methods of both numerical and physical simulation of sea wave groups with the given spectrum and groupiness parameters are suggested.展开更多
Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation ...Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.展开更多
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
Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller b...Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.展开更多
Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lea...Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.展开更多
文摘Based on field wave data, an empirical formula of wave envelope spectrum is given in this paper. Then the methods of both numerical and physical simulation of sea wave groups with the given spectrum and groupiness parameters are suggested.
文摘Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.
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.
基金This project is supported by National Natural Science Foundation of China (No.50205050).
文摘Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.
基金Supported by the Joint Fund of the Ministry of Education of China(Grant No.8091B022203)Youth Talent Support Project(Grant No.2022-JCJQ-QT-059).
文摘Inter-shaft bearing is a crucial supporting component of a dual rotor structure aviation engine.Its structural and operational characteristics make it prone to frequent and highly hazardous faults,which can easily lead to catastrophic accidents such as the failure of the entire rotor system.Therefore,it is significant to realize vibration fault monitoring and warning of inter-shaft bearings.Aero-engine vibration has complex characteristics,with diagnostic methods facing major limitations.Firstly,the compact engine structure and inter-shaft bearing placement cause weak,attenuated fault signals with significant interference,complicating feature extraction.Secondly,in counter-rotating dual-rotor systems,inter-shaft bearing components rotate oppositely,producing higher characteristic frequencies than co-rotating bearings.High-frequency fault signals often overlap or appear harmonic,and their propagation is easily affected by structural complexities,making accurate monitoring challenging.To address these challenges,a method combining Traversal Index Enhanced-gram(TIEgram)and autocorrelation(AC)is proposed for extracting weak fault features in inter-shaft bearings.TIEgram selects the optimal frequency band for resonance demodulation,isolating fault-related signal components.To counter nonstationary signals from aero-engine dynamics,slip-ratio domain order tracking transforms time-domain signals into angular-domain stationary signals.Autocorrelation analysis then yields the squared envelope autocorrelation spectrum,compared with bearing fault characteristic orders for diagnosis.Simulation and experimental results demonstrate the method's effectiveness in extracting weak inter-shaft bearing fault features.
