Study on a Novel Bearing Fault Diagnosis Method from Frequency and Energy Perspective

2017 ◽  
Vol 18 (6) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huiming Zhao ◽  
Wu Deng
Keyword(s):  
Wavelet Packet ◽  
Bottom Layer ◽  
Wavelet Packet Decomposition ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Organic Combination ◽  
Component Faults

AbstractEarly identification of faults in rolling element bearings is a challenging task; especially extracting transient characteristics from a noisy signal and identifying bearings fault become critical steps. In this paper, a novel method for real time fault detection in rolling element bearings is proposed to deal with non-stationary fault signals from frequency and energy perspective. Second-order blind identification (SOBI) and wavelet packet decomposition are organically integrated to diagnose the early bearing faults, the fault vibration signals are processed by SOBI algorithm, and feature information is extracted; meanwhile, fault vibration signals are decomposed by the wavelet packet, the energy of terminal nodes(at the bottom layer of wavelet packet decomposition) are analyzed because the energy of terminal nodes has different sensitive to different component faults. Therefore, the bearing faults can be diagnosed by organic combination of fault characteristic frequency analysis and energy of the terminal nodes, and the effectiveness, feasibility and robustness of the proposed method have been verified by experimental data.

A Novel Bearing Fault Diagnosis Method Based on LMD and Wavelet Packet Energy Entropy

2017 ◽  
Vol 18 (5) ◽  
Author(s):  
Xiumei Li ◽  
Yong Liu ◽  
Huimin Zhao ◽  
Wu Deng ◽  
Yannan Sun
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Peak Frequency ◽  
Band Pass Filter ◽  
Rolling Element Bearings ◽  
Pass Filter ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Rolling Element ◽  
Diagnosis Method

AbstractRolling element bearings faults may lead to fatal breakdown of machines. Therefore, it is significant to be study bearings diagnosis, and the vibration-based methods have received intensive study because vibration signals collected from bearings carry rich information on machine health conditions, and it is possible to obtain vitalcharacteristic information from the vibration signals through using signal processing techniques. This paper proposes a novel vibration-based diagnosis method about bearing faults, first, a new pattern recognition method is proposed to diagnose bearing faults through using the interval value of the spectral peak frequency in the frequency domain; second, vibration signals of different parts faults of the bearings will be processed by different algorithm for precisely extracting the fault characteristics; and third, in order to extract transient characteristics from a noisy signal, the filter need to be developed and to further improve the signal-to-noise ratio (SNR), band pass filter is designed based on the PSD of vibration signals in this paper. The vibration signals collected from rolling element bearings are used to demonstrate the performance of the proposed method, andthe results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings.

A multichannel fusion approach based on coupled hidden Markov models for rolling element bearing fault diagnosis

2011 ◽  
Vol 226 (1) ◽  
pp. 202-216 ◽  
Author(s):  
W B Xiao ◽  
J Chen ◽  
G M Dong ◽  
Y Zhou ◽  
Z Y Wang
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Wavelet Packet ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Coupled Hidden Markov Models ◽  
Element Bearing ◽  
Fusion Approach

This paper presents a novel multichannel fusion approach based on coupled hidden Markov models (CHMMs) for rolling element bearing fault diagnosis. Different from a hidden Markov model (HMM), a CHMM contains multiple state sequences and observation sequences, and hence has powerful potential for multichannel fusion. In this study, a two-chain CHMM is employed to integrate the two-channel vibration signals collected from bearings, i.e. the horizontal and vertical vibration signals. Efficient probabilistic inference and parameter estimation algorithms are developed for the model. An experiment was carried out to validate the proposed approach. Normalized wavelet packet energy and wavelet packet energy entropy are extracted as features for classification respectively. Then, the results of the proposed approach are compared with those of the currently used approach based on HMMs and one-channel signals. The results show that the proposed approach is feasible and effective to improve the classification rate.

scholarly journals Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Weigang Wen ◽  
Zhaoyan Fan ◽  
Donald Karg ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Fractal Dimensions ◽  
Feature Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Yaguo Lei ◽  
Zhengjia He ◽  
Yanyang Zi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Rolling Element Bearings ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Multiple Classifiers ◽  
Operation Conditions ◽  
Rolling Element

This paper presents a new method for fault diagnosis of rolling element bearings, which is developed based on a combination of weighted K nearest neighbor (WKNN) classifiers. This method uses wavelet packet transform based on the lifting scheme to preprocess the vibration signals before feature extraction. Time- and frequency-domain features are all extracted to represent the operation conditions of the bearings totally. Sensitive features are selected after feature extraction. And then, multiple classifiers based on WKNN are combined to overcome the two disadvantages of KNN and therefore it may enhance the classification accuracy. The experimental results of the proposed method to fault diagnosis of the rolling element bearings show that this method enables the detection of abnormalities in bearings and at the same time identification of fault categories and levels.

Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters

2002 ◽  
Vol 8 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Zhidong Chen ◽  
Chris K. Mechefske
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Machine Condition ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Model Based ◽  
Machine Condition Monitoring ◽  
Signal Parameters ◽  
Rolling Element ◽  
Efficient Machine

This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing transient vibration signals. An example is included that shows how the procedure was employed to analyse the transient vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration transient vibration signals are being generated.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine

2019 ◽  
Vol 41 (14) ◽  
pp. 4013-4022 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

Multi-scale fuzzy entropy (MFE) is a recently developed non-linear dynamic parameter for measuring the complexity of vibration signals of rolling element bearing over different scales. However, the calculation of fuzzy entropy (FuzzyEn) in each scale ignores the sequence’s global characteristics while the bearing vibration signals’ global fluctuation may vary as the bearing runs under different states. Therefore, in this paper, the multi-scale global fuzzy entropy (MGFE) method is put forward for extracting the fault features from the bearing vibration signals. After the feature extraction, multiple class feature selection (MCFS) method is introduced to select the most informative features from the high-dimensional feature vector. Then, a new rolling element bearing fault diagnosis approach is proposed based on MGFE, MCFS and support vector machine (SVM). The experimental results indicate that the proposed approach can effectively fulfill the fault diagnosis of rolling element bearing and has good classification performance.

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