CYCLOSTATIONARY ANALYSIS OF ROLLING-ELEMENT BEARING VIBRATION SIGNALS

2001 ◽  
Vol 248 (5) ◽  
pp. 829-845 ◽  
Author(s):  
I. ANTONIADIS ◽  
G. GLOSSIOTIS
Keyword(s):  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

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.

scholarly journals An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1079
Author(s):  
Guoping An ◽  
Qingbin Tong ◽  
Yanan Zhang ◽  
Ruifang Liu ◽  
Weili Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Optimization Problem ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Optimal Mode ◽  
Fault Feature Extraction

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.

Cyclostationary Analysis of Rolling Element Bearing Vibration Signals

2001 ◽  
Author(s):  
George N. Glossiotis ◽  
Ioannis A. Antoniadis
Keyword(s):  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Signal Features ◽  
Rolling Element ◽  
Physical Information ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Signal Processing Methods ◽  
Rich Content ◽  
Proper Analysis

Abstract Vibration signals resulting from rolling element bearings present a rich content of physical information, the proper analysis of which can lead among others to the identification of possible faults. Traditionally, this analysis is performed by the use of signal processing methods, which assume statistically stationary signal features. The paper proposes an alternative framework for analyzing bearing vibration signals, based on cyclostationary analysis. This framework, being able to model additionally signals with periodically varying statistics, is better able to exhibit the underlying physical concepts of the modulation mechanism, present in the vibration response of bearings. The basic concepts of the approach are demonstrated both in illustrative simulation results, as well as in experimental results and industrial measurements for two different types of bearing faults.

scholarly journals Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 926 ◽  
Author(s):  
Keheng Zhu ◽  
Liang Chen ◽  
Xiong Hu
Keyword(s):  
Fuzzy Entropy ◽  
Rolling Element Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Iterative Filtering

A new fault feature extraction method for rolling element bearing is put forward in this paper based on the adaptive local iterative filtering (ALIF) algorithm and the modified fuzzy entropy. Due to the bearing vibration signals’ non-stationary and nonlinear characteristics, the ALIF method, which is a new approach for the analysis of the non-stationary signals, is used to decompose the original vibration signals into a series of mode components. Fuzzy entropy (FuzzyEn) is a nonlinear dynamic parameter for measuring the signals’ complexity. However, it only emphasizes the signals’ local characteristics while neglecting its global fluctuation. Considering the global fluctuation of bearing vibration signals will change with the bearing working condition varying, we modified the FuzzyEn. The modified FuzzyEn (MFuzzyEn) of the first few modes obtained by the ALIF is utilized to form the fault feature vectors. Subsequently, the corresponding feature vectors are input into the multi-class SVM classifier to accomplish the bearing fault identification automatically. The experimental analysis demonstrates that the presented ALIF-MFuzzyEn-SVM approach can effectively recognize the different fault categories and different levels of bearing fault severity.

Sparse Representation of Vibration Signals Using Trained Dictionary

2014 ◽  
Vol 574 ◽  
pp. 690-695 ◽  
Author(s):  
Jun Feng Guo ◽  
Xiao Hui Zheng ◽  
Xing Chun Wei ◽  
Rui Cheng Feng
Keyword(s):  
Sparse Representation ◽  
Matching Pursuit ◽  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Compression Performance ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Simulation Results

The sparsity of a signal is critical for compression and high compression performance is obtained by utterly sparse signal. But real signals are not sparse commonly, so sparse transformation is considered. In addition, the sparse degree of coefficients is mainly determined by the quality of transform base. Therefore, this paper constructs a transform base (trained dictionary) by K-SVD algorithm for rolling element bearing vibration signals and uses the Orthogonal Matching Pursuit (OMP) algorithm to conduct sparse representation and simulation. Results show that the trained dictionary can be more fitted with the features of signals, the residual components are smaller and the reconstruct similarity is higher compared to the untrained dictionaries, obtaining better representation.

scholarly journals Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation

2013 ◽  
Vol 20 (2) ◽  
pp. 213-225 ◽  
Author(s):  
W.Y. Liu ◽  
J.G. Han
Keyword(s):  
Rolling Element Bearing ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Fault Recognition ◽  
Binary Feature ◽  
Training Samples ◽  
Bispectrum Estimation ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration

A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct.

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.

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 Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhipeng Feng ◽  
Fulei Chu
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cyclic Frequency ◽  
Fm Signals ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Cyclic Spectrum

Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

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