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

2013 ◽  
Vol 694-697 ◽  
pp. 1155-1159
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Yan Jie Zhou ◽  
Ya Song Pu
Keyword(s):  
Energy Distribution ◽  
Fault Identification ◽  
Morphological Filter ◽  
Original Signal ◽  
Energy Distributions ◽  
Vibration Signals ◽  
Fault Pattern ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

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.

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

2013 ◽  
Vol 706-708 ◽  
pp. 1397-1400 ◽  
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Yan Jie Zhou ◽  
Jie Min
Keyword(s):  
Rank Order ◽  
Sample Entropy ◽  
Recognition Method ◽  
Vibration Signals ◽  
Fault Pattern ◽  
Fault Recognition ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Novel Method ◽  
Gear Fault Diagnosis

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.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1151-1154
Author(s):  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Jia Xing Zhu ◽  
Yan Ping Su
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

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.

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

2013 ◽  
Vol 347-350 ◽  
pp. 430-433
Author(s):  
Wen Bin Zhang ◽  
Jia Xing Zhu ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Rank Order ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Small Sample ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Gearbox Vibration

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.

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

Application of S transform and morphological pattern spectrum for gear fault diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2963-2972 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
Z-J Wang ◽  
S-S Mi ◽  
D-S Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Transforms ◽  
Morphological Pattern ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
S Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.

Gear Fault Diagnosis Method Based on Cyclostationary Degree and HMM

2011 ◽  
Vol 291-294 ◽  
pp. 3397-3400
Author(s):  
Bin Wu ◽  
Jing Kang ◽  
Yue Gang Luo
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Training Sample ◽  
Fault Identification ◽  
Fm Signals ◽  
Recognition Result ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis ◽  
Working Status

This paper proposes a gear fault diagnosis method based on cyclostationary degree and Hidden Markov Model (HMM) theory. By using the demodulation characteristic of cyclostationary degree for AM and FM signals, we extract the characteristic information of the gear working status. This information can be converted into sets of fault feature vector, which is used as the training sample or observed sample of HMM model for gear fault identification. Experimental results show that the gear fault diagnosis method has good recognition result for the four kinds of status of normal, broken teeth, pitting and wear gear.

Multi-scale fractal dimension based on morphological covering for gear fault diagnosis

2011 ◽  
Vol 225 (9) ◽  
pp. 2242-2249 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
S-S Mi ◽  
Y-T Zhang ◽  
D-S Liu
Keyword(s):  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Fractal Structures ◽  
Statistical Parameters ◽  
Classification Rate ◽  
Vibration Signals ◽  
Multi Scale ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Vibration Signal Analysis

Fractal dimension (FD) is one of the most utilized parameters for characterizing and discriminating vibration signals in gear fault detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant FD at all scales may not be appropriate. Motivated by this fact, this article explores the capacity of the multi-scale fractal dimension (MFD) to represent the complexity of vibration signals for gear fault diagnosis. We select the morphological covering method to calculate the MFD. Vibration signals measured from a gear test rig with five states are employed to evaluate the effectiveness of the presented method. Experimental results reveal that the vibration signals acquired from gear with five states demonstrate different fractal structures when the visualization scales are changed. The MFD can provide more information about the signals and yield a higher classification rate than the FD and traditional statistical parameters. It is very reasonable to apply the MFD to vibration signal analysis for improving the performance of the gear fault diagnosis.

The Application of Principal-Component Analysis in Gear Fault Diagnosis

1991 ◽  
Author(s):  
Zhen-Ying Zhao ◽  
Jian-Zhong Cha ◽  
He Tang ◽  
Meng-Zhou Zhu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Vibration Signals ◽  
Principal Component Method ◽  
Gear Noise ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Abstract In this paper, the application of the principal-component analysis method in fault diagnosis is explored. Characterized as fast and precise, this method can be directly used for analyzing gear noise and vibration signals in time domain. The principal component method and its1 error occur-ring in the calculation are theoretically discussed in detail. A program for implementing this method has been developed and the experiments for gear fault diagnosis have been carried out with satisfactory results.

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