scholarly journals Characterizing vibration signals utilizing morphological pattern spectrum for gear fault diagnosis

2015 ◽  
Author(s):  
Bing Li ◽  
Min Gao ◽  
Qingchen Guo ◽  
Fang Liu
Keyword(s):  
Fault Diagnosis ◽  
Morphological Pattern ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Gear Fault ◽  
Gear Fault Diagnosis

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 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.

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.

TIME-FREQUENCY ANALYSIS BY EVOLUTIONARY PERIODOGRAM WITH APPLICATION IN GEAR FAULT DIAGNOSIS

2010 ◽  
Vol 08 (05) ◽  
pp. 679-693 ◽  
Author(s):  
M. A. AL-MANIE ◽  
W. J. WANG
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Background Noise ◽  
Alternative Technique ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Single Function ◽  
Gear Fault ◽  
Gear Fault Diagnosis

The evolutionary periodogram has been introduced to mechanical fault diagnosis and relationship between the evolutionary periodogram and time-frequency spectrogram has been investigated. The evolutionary periodogram is unveiled as an especially windowed spectrogram, and is applied to gearbox fault diagnosis. It has been shown that the window used in the evolutionary periodogram is not a single function but a combination of a set of functions. Two cases of gearbox diagnosis are presented as examples of application. Vibration signals and a synchronous signal are collected for the analysis. The time synchronous averaging is used to reduce background noise or random transients to enhance the periodicity of a specific gear rotation. The performance of the evolutionary periodogram has been compared with the spectrogram for gear diagnosis, showing that the evolutionary periodogram is an alternative technique in time-frequency analysis for fault detection and better resolution can be obtained as more choices are offered by the way of constructing the window.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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