scholarly journals Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 730 ◽  
Author(s):  
Mingtao Ge ◽  
Jie Wang ◽  
Yicun Xu ◽  
Fangfang Zhang ◽  
Ke Bai ◽  
...  
Keyword(s):  
Gaussian Distribution ◽  
Gaussian Noise ◽  
Hypothesis Test ◽  
Signal Reconstruction ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Outer Race ◽  
Empirical Wavelet Transform ◽  
Non Stationary Signal

Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing’s stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.

scholarly journals Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Numerical Simulations ◽  
Rolling Bearing ◽  
High Order ◽  
Rolling Bearings ◽  
Theoretical Derivation ◽  
Denoising Method ◽  
Vibration Signals ◽  
Local Projection ◽  
Ring Fault

Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

Fault Diagnosis Methods of Rolling Bearing: A General Review

2011 ◽  
Vol 480-481 ◽  
pp. 986-992 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Jian Wei Ji ◽  
Xiao Wei Han
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Rolling Bearings ◽  
Signal Process ◽  
Existing Problems ◽  
Bearing Failures ◽  
Non Stationary Signal ◽  
Fault Mechanism ◽  
Research Situation

Rolling bearing failures account for most of rotating machinery failures. Fault diagnosis of rolling bearings according to their running state is of great importance. In this paper current research situation and existing problems of fault diagnosis are summarized firstly. Then several different diagnosis approaches in terms of the measuring medium are reviewed. After analysis of fault mechanism, feature extraction based on non-stationary signal process is elaborated. Finally, the development tendencies are pointed out.

scholarly journals Fault Diagnosis of Rolling Bearing Using Multiscale Amplitude-Aware Permutation Entropy and Random Forest

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 184 ◽  
Author(s):  
Chen ◽  
Zhang ◽  
Zhao ◽  
Luo ◽  
Sun
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Rolling Bearing ◽  
Identification Accuracy ◽  
Coarse Grained ◽  
Permutation Entropy ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Fault Severity

A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

2014 ◽  
Vol 912-914 ◽  
pp. 873-877 ◽  
Author(s):  
Feng Kui Cui ◽  
Fei Fei Lv ◽  
Xiao Qiang Wang ◽  
Dong Ying Zhang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Noise Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Noise Interference ◽  
Low Snr ◽  
Contrast Analysis

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map

2020 ◽  
Vol 26 (21-22) ◽  
pp. 1886-1897 ◽  
Author(s):  
Jialing Zhang ◽  
Jimei Wu ◽  
Bingbing Hu ◽  
Jiahui Tang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
The Self ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Map ◽  
Mode Decomposition ◽  
Self Organizing

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.

scholarly journals Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races

2021 ◽  
Vol 1207 (1) ◽  
pp. 012006
Author(s):  
Wei Luo ◽  
Changfeng Yan ◽  
Junbao Yang ◽  
Yaofeng Liu ◽  
Lixiao Wu
Keyword(s):  
Rolling Bearing ◽  
Vibration Response ◽  
Radial Clearance ◽  
Rolling Bearings ◽  
Outer Race ◽  
Response Characteristics ◽  
Bearing Fault Diagnosis ◽  
Theoretical Criterion ◽  
Vibration Model ◽  
Rolling Elements

Abstract Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.

scholarly journals Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

2019 ◽  
Vol 9 (8) ◽  
pp. 1681 ◽  
Author(s):  
Cui ◽  
Du ◽  
Yang ◽  
Xu ◽  
Song
Keyword(s):  
Rolling Bearing ◽  
Rolling Bearings ◽  
Outer Race ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Q Factors ◽  
The Impact ◽  
Fault Information ◽  
Fault Characteristic

Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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