Wenbin Zhang

Application of rank-order morphological filter in vibration signal de-noising

This paper introduced the good characteristic of rank-order morphological filter and its application in vibration signal de-noising. At first, the definition of rank-order morphological filter was introduced. Then, the combined filters were constructed by the rank-order opening and rank-order closing. At last, the interrupted vibration signal was de-noised by the combined rank-order morphological filters. Practical and simulation results show that this method has better de-noising effectiveness. Its suitable for on-line monitoring and diagnosis of rotating machinery.

Application of rank-order morphological filter in refinement of rotor center's orbit

This paper introduced the good characteristic of rank-order morphological filter and its application in refinement of rotor centers orbit. Firstly, the definition of rank-order morphological filter was introduced. Secondly, the combined filters were constructed by the rank-order opening and rank-order closing. Finally, the interrupted rotor centers orbit was refined by the combined rank-order morphological filters. Simulation and practical results show that this approach has better refinement effectiveness in rotor centers orbit. Its suitable for on-line monitoring and diagnosis of rotating machinery.

A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

A New Intelligent Fault Recognition Method for Rotating Machinery

In this paper, a novel method to recognize rotor fault pattern was proposed based on rank-order morphological filter, harmonic window decomposition, sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Then, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that needs not decomposition; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different rotor vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in fault diagnosis of rotating machinery effectively.

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

In this paper, a novel method to recognize gear fault pattern was approached based on harmonic wavelet package (HWP), sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

Application of EEMD Sample Entropy and Grey Relation Degree in Gearbox Fault Identification

In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. First, the sampled data was de-noised by mathematical morphological filter. Second, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Third, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, due to the grey relation degree has good classify capacity for small sample pattern identification; the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. Its suitable for on-line monitoring and diagnosis of gearbox.

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

A New Intelligent Fault Recognition Method for Gearbox

In this paper, a novel method to recognize gear fault pattern was approached based on multi-scale morphological undecimated wavelet decomposition, sample entropy and grey incidence. Firstly, multi-scale morphological undecimated wavelet decomposition was developed based on the characteristic of impulse feature extraction in difference morphological filter. And it was used to process different gear fault signals in five levels. Secondly, the sample entropy of each level was calculated. Finally, the sample entropy was served as the feature vectors and the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical example shows the efficiency of the proposed recognition method. It is suitable for on-line monitoring and fault diagnosis of gear.

Intelligent Gear Fault Identification Method Based on Harmonic Wavelet Package Energy Distribution and Grey Incidence

In this paper, a novel intelligent method to identify gear fault pattern was approached based on morphological filter, harmonic wavelet package and grey incidence. At first, the line structure element was selected for morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and energy distribution of each band was calculated. Finally, these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.