scholarly journals A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3994 ◽  
Author(s):  
Dong Zhen ◽  
Junchao Guo ◽  
Yuandong Xu ◽  
Hao Zhang ◽  
Fengshou Gu
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Weighted Average ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Vibration Signature ◽  
Stationary Vibration

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

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.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

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.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

scholarly journals Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Xu ◽  
Gang Tang ◽  
Mengfu He
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Frequency Noise ◽  
Wavelet Method ◽  
Bearing Fault ◽  
Feature Enhancement ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Vibration Signal Analysis

Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method.

scholarly journals Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
M. Buzzoni ◽  
E. Mucchi ◽  
G. D’Elia ◽  
G. Dalpiaz
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Condition Indicators ◽  
Intermediate Shaft ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Vibration Signature ◽  
Localized Faults

The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson’s coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

scholarly journals Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254747
Author(s):  
Kangping Gao ◽  
Xinxin Xu ◽  
Jiabo Li ◽  
Shengjie Jiao ◽  
Ning Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Comprehensive Evaluation ◽  
Polycrystalline Diamond ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Polycrystalline Diamond Compact

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

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