It has been challenging to correctly separate the mixed signals into source components when the source number is not known a priori.To reveal the complexity of the measured vibration signals,and provide the priori inf...It has been challenging to correctly separate the mixed signals into source components when the source number is not known a priori.To reveal the complexity of the measured vibration signals,and provide the priori information for the blind source separation,in this paper,we propose a novel source number estimation based on independent component analysis(ICA)and clustering evaluation analysis,and then carry out experiment studies with typical mechanical vibration signals from a shell structure.The results demonstrate that the proposed ICA based source number estimation performs stably and robustly for the shell structure.展开更多
The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibrati...The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibration measurements,the critical fault signatures are always masked by overwhelming interfering contents,therefore difficult to be identified.Moreover,owing to the distinguished time-frequency characteristics of the machinery fault signatures,classical dyadic wavelet transforms(DWTs) are not perfect for detecting them in noisy environments.In order to address the deficiencies of DWTs,a pseudo wavelet system(PWS) is proposed based on the filter constructing strategies of wavelet tight frames.The presented PWS is implemented via a specially devised shift-invariant filterbank structure,which generates non-dyadic wavelet subbands as well as dyadic ones.The PWS offers a finer partition of the vibration signal into the frequency-scale plane.In addition,in order to correctly identify the essential transient signatures produced by the faulty mechanical components,a new signal impulsiveness measure,named spatial spectral ensemble kurtosis(SSEK),is put forward.SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized.The proposed method has been applied to engineering fault diagnosis cases,in which the processing results showed its effectiveness and superiority to some existing methods.展开更多
基金supported by China Postdoctoral Science Foundation (No. 2013M532032)National Nature Science Foundation of China (No. 51305329, 51035007)+1 种基金the Doctoral Foundation of Education Ministry of China (No. 20130201120040)the Shaanxi Postdoctoral Scientific research project
文摘It has been challenging to correctly separate the mixed signals into source components when the source number is not known a priori.To reveal the complexity of the measured vibration signals,and provide the priori information for the blind source separation,in this paper,we propose a novel source number estimation based on independent component analysis(ICA)and clustering evaluation analysis,and then carry out experiment studies with typical mechanical vibration signals from a shell structure.The results demonstrate that the proposed ICA based source number estimation performs stably and robustly for the shell structure.
基金supported financially by the National Natural Science Foundation of China(Grant Nos.51275382 and 11176024)the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No.20110201130001)
文摘The rotating machinery,as a typical example of large and complex mechanical systems,is prone to diversified sorts of mechanical faults,especially on their rotating components.Although they can be collected via vibration measurements,the critical fault signatures are always masked by overwhelming interfering contents,therefore difficult to be identified.Moreover,owing to the distinguished time-frequency characteristics of the machinery fault signatures,classical dyadic wavelet transforms(DWTs) are not perfect for detecting them in noisy environments.In order to address the deficiencies of DWTs,a pseudo wavelet system(PWS) is proposed based on the filter constructing strategies of wavelet tight frames.The presented PWS is implemented via a specially devised shift-invariant filterbank structure,which generates non-dyadic wavelet subbands as well as dyadic ones.The PWS offers a finer partition of the vibration signal into the frequency-scale plane.In addition,in order to correctly identify the essential transient signatures produced by the faulty mechanical components,a new signal impulsiveness measure,named spatial spectral ensemble kurtosis(SSEK),is put forward.SSEK is used for selecting the optimal analyzing parameters among the decomposed wavelet subbands so that the masked critical fault signatures can be explicitly recognized.The proposed method has been applied to engineering fault diagnosis cases,in which the processing results showed its effectiveness and superiority to some existing methods.
