A New Method EWT-Based for Rolling Element Bearing Weak Fault Diagnosis

2015 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Chun Cheng ◽  
Aijuan Li
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Scale Space ◽  
New Method ◽  
Rolling Element Bearing ◽  
Space Representation ◽  
Empirical Wavelet Transform ◽  
Rolling Element ◽  
Otsu’S Method ◽  
Weak Fault

The collected vibration signals from defective rolling element bearings are generally non-stationary and corrupted by strong background noise. The weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. A new method EWT-based (Empirical Wavelet Transform) for bearing fault diagnosis is proposed in this paper. It consists of four parts. Firstly, the frequency ranges of meaningful modes are self-adaptively obtained by combining scale-space representation and Otsu’s method. Secondly, the meaningful modes are acquired by utilizing EWT to decompose the raw vibration signal. Thirdly, the first two modes possessing maximum kurtosis are selected as fault components. Lastly, the fault-related features could be observed in the time domain and envelope spectra of the selected modes. Experimental results verify that the proposed method is very effective for bearing weak fault diagnosis and the performance of proposed method is obviously better than the method of empirical mode decomposition (EMD).

scholarly journals Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 350-358
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Machine Learning Techniques ◽  
Ensemble Classifiers ◽  
Statistical Features ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing

Rolling element bearing health condition is monitored by analysing its vibration signature. Raw vibration signal picked up through suitably placed accelerometers is difficult to analyse hence many signal processing techniques have been proposed and developed by researchers to process the data for suitably extracting an effective signal feature set. Various machine learning techniques have been used for interpretation and accurate fault diagnosis using this extracted feature set. In this study “Empirical mode decomposition” is used for pre-processing the raw vibration data. Six “Statistical features” are extracted from the best Intrinsic mode function obtained through EMD and “Ensemble machine learning classifiers” are used for bearing fault diagnosis. A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness

Fault Diagnosis of Bearings Based on Time-Delayed Correlation Demodulation and Fuzzy Clustering

2011 ◽  
Vol 211-212 ◽  
pp. 510-514 ◽  
Author(s):  
Pan Fu ◽  
Wei Lin Li ◽  
Wei Qing Cao
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Failure Diagnosis ◽  
Rolling Element Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Set Up

As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore, great attentions have been attributed to the theories, failure diagnosis methods and their applications for rolling bearings. Vibration analysis is also a very important means for bearing fault diagnosis. This paper aims at the research on the methods of signal processing and pattern recognition. An experimental platform was set up for the failure diagnosis of rolling bearings, on which we have done a lot of experiments. Then the vibration signals of normal rolling bearings, rolling bearings with failure on the outer and inner race were collected. Time-delayed correlation demodulation was applied and the features of vibration signal were effectively extracted. Fuzzy C-means clustering system was established to carry out the recognition of the fault of bearings. Experimental results have proved the developed fault diagnostic architecture is reliable and effective.

scholarly journals A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Teager Energy Operator ◽  
Rolling Element ◽  
Element Bearing ◽  
Teager Energy

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

Fault diagnosis of rolling element bearing weak fault based on sparse decomposition and broad learning network

2019 ◽  
Vol 42 (2) ◽  
pp. 169-179
Author(s):  
Xiaocheng Li ◽  
Jingcheng Wang ◽  
Bin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Learning System ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
High Resonance ◽  
Output Layer ◽  
Rolling Element ◽  
Input Layer ◽  
The Impact

Rolling element bearings are widely used in rotating machinery and, at the same time, they are easily damaged due to harsh operating environments and conditions. As a result, rolling element bearings are critical to the safe operation of the mechanical devices. The incipient fault information extraction of rolling bearings mainly faces the following difficulties: (1) The fault signal is too weak. (2) The fault mechanism and the dynamic model of the rolling bearing system are complex. (3) The oscillations caused by the fault shocks are overlapped due to the smaller impact between two adjacent faults. (4) The impact interval of the fault will change randomly. To overcome the aforementioned difficulties, a connection network constructed by resonance-based sparse signal decomposition (RSSD) and broad learning system (BLS) without the need for deep architecture, namely RSSD-BLS, is proposed for intelligent fault diagnosis. We construct RSSD-BLS by input layer, RSSD decomposition layer, feature layer and output layer. So, when the observed vibration signals are the input layer, the network first uses RSSD to decompose the raw vibration signal into high resonance components and low resonance components. Then, the network obtains energy spectrum features of high resonance components which decomposed by RSSD to extract the unique features in the feature. Finally, the network recognizes different fault conditions in the output layer. Through comparing with commonly used intelligent network diagnosis method, the superiority of the proposed RSSD-BLS is verified.

Rolling Element Bearing Fault Diagnosis Using Complex Gaussian Wavelet

2010 ◽  
Author(s):  
P. K. Kankar ◽  
Satish C. Sharma ◽  
S. P. Harsha
Keyword(s):  
Fault Diagnosis ◽  
High Performance ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Wavelet Coefficients ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Rolling Element

This paper is focused on fault diagnosis of bearings due to localized defects i.e. spall on the bearing components, which is essential to the design of high performance rotor bearing system. The methodology proposed in this paper for fault diagnosis of rolling element bearings, utilizes autocorrelation of raw vibration signals to reduce the dimension of vibration signals with minimal loss of significant frequency content. Dimension of vibration signal is reduced to 10% with negligible loss of information. To extract most appropriate features from auto-correlated vibration signals and for effective classification of faults, vibration signals are decomposed using complex Gaussian wavelet. Total 150 signals of healthy and defective bearings at rotor speeds 250, 500, 1000, 1500 and 2000 rpm with three loading conditions are considered. 1-D continuous wavelet coefficients of these samples are calculated at the seventh level of decomposition (27 scales for each sample). Maximum Energy to Shannon Entropy ration criterion is used to determine scale corresponding to characteristic defect frequency. Statistical features are extracted from the wavelet coefficients corresponding to selected scales. Finally, bearing faults are classified using Support Vector Machine (SVM) method. The test results show that the SVM can be used efficiently for bearing fault classification. It is also observed that classification accuracy is improved by using autocorrelation.

A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis

2018 ◽  
Vol 233 (7) ◽  
pp. 2574-2587 ◽  
Author(s):  
Heng-di Wang ◽  
Si-er Deng ◽  
Jian-xi Yang ◽  
Hui Liao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Foundation Vibration ◽  
Diagnosis Method ◽  
Element Bearing ◽  
Vibration Signal Analysis

Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within [Formula: see text] are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.

Contact characteristic and vibration mechanism of rolling element bearing in the process of fault evolution

2021 ◽  
Vol 235 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Wenbing Tu ◽  
Jinwen Yang ◽  
Wennian Yu ◽  
Ya Luo
Keyword(s):  
Fault Diagnosis ◽  
Vibration Response ◽  
Rolling Element Bearing ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Vibration Mechanism ◽  
Contact Characteristic ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Fault Evolution

The vibration response of rolling element bearing has a close relation with its fault. An accurate evaluation of the bearing vibration response is essential to the bearing fault diagnosis. At present, most bearing dynamics models are built based on rigid assumptions, which may not faithfully reveal the dynamic characteristics of bearing in the presence of fault. Moreover, previous similar works mainly focus on the fault with a specified size without considering the varying contact characteristics as the fault evolves. This paper developed an explicit dynamics finite element model for the bearing with three types of raceway faults considering the flexibility of each bearing component in order to accurately study the contact characteristic and vibration mechanism of defective bearings in the process of fault evolution. The developed model is validated by comparing its simulation results with both analytical and experimental results. The dynamic contact patterns between the rolling elements and the fault, the additional displacement due to the fault and the faulty characteristics within the bearing vibration signal during the fault evolution process are investigated. The analysis results from this work can provide practitioners an in-depth understanding towards the internal contact characteristics with the existence of raceway fault and theoretical basis for rolling bearing fault diagnosis.

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