The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibrat...The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.展开更多
Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear b...Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear box were identified. The research indicates that the hoist＇s fault information is non-stationary, and non-stationary signal is clearly extracted by using db20 wavelet as basis function. The db20 wavelet is the proper wavelet base for vibration signal analysis of the hoist gear box.展开更多
Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each oth...Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.展开更多
To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization （BBPSO） algorith...To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization （BBPSO） algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.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Projects(51375484,51475463)supported by the National Natural Science Foundation of ChinaProject(kxk140301)supported by Interdisciplinary Joint Training Project for Doctoral Student of National University of Defense Technology,China
文摘The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.
文摘Using wavelets, the vibration signal of a certain mine hoist gear box was analyzed. By multiple comparison analysis, the rational wavelet basis function was determined. Fault characteristic frequencies of hoist gear box were identified. The research indicates that the hoist＇s fault information is non-stationary, and non-stationary signal is clearly extracted by using db20 wavelet as basis function. The db20 wavelet is the proper wavelet base for vibration signal analysis of the hoist gear box.
文摘Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.
文摘To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization （BBPSO） algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.
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.
