A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis

2022 ◽  
Vol 164 ◽  
pp. 108216
Author(s):  
Qing Ni ◽  
J.C. Ji ◽  
Ke Feng ◽  
Benjamin Halkon
Keyword(s):  
Rolling Element Bearings ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Fault Information

scholarly journals Fault Diagnosis of Rolling Element Bearings with a Two-Step Scheme Based on Permutation Entropy and Random Forests

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 96 ◽  
Author(s):  
Xiaoming Xue ◽  
Chaoshun Li ◽  
Suqun Cao ◽  
Jinchao Sun ◽  
Liyan Liu
Keyword(s):  
Random Forests ◽  
Classification Model ◽  
Permutation Entropy ◽  
Rolling Element Bearings ◽  
Variational Mode Decomposition ◽  
Statistical Classification ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Single Scale ◽  
Rolling Element

This study presents a two-step fault diagnosis scheme combined with statistical classification and random forests-based classification for rolling element bearings. Considering the inequality of features sensitivity in different diagnosis steps, the proposed method utilizes permutation entropy and variational mode decomposition to depict vibration signals under single scale and multiscale. In the first step, the permutation entropy features on the single scale of original signals are extracted and the statistical classification model based on Chebyshev’s inequality is constructed to detect the faults with a preliminary acquaintance of the bearing condition. In the second step, vibration signals with fault conditions are firstly decomposed into a collection of intrinsic mode functions by using variational mode decomposition and then multiscale permutation entropy features derived from each mono-component are extracted to identify the specific fault types. In order to improve the classification ability of the characteristic data, the out-of-bag estimation of random forests is firstly employed to reelect and refine the original multiscale permutation entropy features. Then the refined features are considered as the input data to train the random forests-based classification model. Finally, the condition data of bearings with different fault conditions are employed to evaluate the performance of the proposed method. The results indicate that the proposed method can effectively identify the working conditions and fault types of rolling element bearings.

A fault diagnosis method of rolling element bearings based on CEEMDAN

2015 ◽  
Vol 231 (10) ◽  
pp. 1804-1815 ◽  
Author(s):  
Yaguo Lei ◽  
Zongyao Liu ◽  
Julien Ouazri ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Noisy Signals ◽  
Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction

2019 ◽  
Vol 42 (3) ◽  
pp. 518-527 ◽  
Author(s):  
Hua Li ◽  
Tao Liu ◽  
Xing Wu ◽  
Qing Chen
Keyword(s):  
Resonance Frequency ◽  
Rolling Bearing ◽  
Adaptive Signal Processing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Fault Type ◽  
Mode Decomposition ◽  
Adaptive Signal ◽  
Fault Feature Extraction ◽  
Fault Information

Variational mode decomposition (VMD) is an adaptive signal processing method proposed recently. It has gradually been widely used due to its good performance. According to the problem that the parameters of VMD need to be determined in advance, a simple and feasible method of determining the influence parameters based on the principle of kurtosis maximum is put forward. A novel intrinsic mode function (IMF) selection method based on resonance frequency is proposed in order to select the IMF that contains the abundant fault feature information. Firstly, the parameters of VMD are optimized by the principle of kurtosis maximum, the optimal penalty parameter and mode number of VMD are set, and the original fault signal is processed by the optimized VMD to obtain the established IMF components. Then, the sensitive IMF(s) with the fault information is selected by resonance frequency. Finally, the selected IMF(s) is analyzed by the envelope demodulation analysis to extract the fault characteristic frequency to judge the fault type of the rolling bearing. It is shown that the method can extract the weak characteristics of the early fault signal of the rolling bearing, and it can realize the judgment of the bearing fault accurately through the analysis of simulated signal and the actual data of bearing.

scholarly journals Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine

2014 ◽  
Vol 6 ◽  
pp. 803919 ◽  
Author(s):  
Jianzhong Zhou ◽  
Jian Xiao ◽  
Han Xiao ◽  
Weibo Zhang ◽  
Wenlong Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Rolling Element Bearings ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Learning Machine

This paper presented a novel procedure based on the ensemble empirical mode decomposition and extreme learning machine. Firstly, EEMD was utilized to decompose the vibration signals into a number of IMFs adaptively and the permutation entropy of each IMF was calculated to generate the fault feature matrix. Secondly, a new extreme learning machine was proposed by combining ensemble extreme learning machine and the evolutionary extreme learning machine which used an artificial bee colony algorithm to optimize the input weights and hidden bias. The proposed diagnosis algorithm was applied on the three rolling bearing fault diagnosis experiments. The numerical experimental results demonstrated that the proposed method had an improved generalization performance than traditional extreme and other variants.

Pedestal looseness fault diagnosis in a rotating machine based on variational mode decomposition

2016 ◽  
Vol 231 (13) ◽  
pp. 2493-2502 ◽  
Author(s):  
Xueli An ◽  
Fei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotor System ◽  
Variational Mode Decomposition ◽  
Rotor Vibration ◽  
Stationary Characteristic ◽  
Rotating Machine ◽  
Mode Decomposition ◽  
Fault Features ◽  
Fault Information

According to the non-stationary characteristic of rotating machinery vibration signals of a rotor system with a loose pedestal fault, variational mode decomposition was applied in the pedestal looseness fault diagnosis for such a rotor system. Variational mode decomposition is used to decompose the rotor vibration signal into several stable components. This can achieve the separation of the pedestal looseness fault signal from the background signals, and extract the fault characteristic of a vibration signal from a rotor system with pedestal looseness. Experimental data from a rotor system with pedestal looseness were used to verify the proposed method. The results showed that the stable components of the rotor vibration signal obtained by variational mode decomposition have obvious amplitude modulation characteristics. The components which contain fault information were analyzed by envelope demodulation, which can extract the pedestal looseness fault features of a rotor vibration signal. Therefore, the variational mode decomposition method can be effectively applied to the pedestal looseness fault diagnosis of such a rotor system.

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

2018 ◽  
Vol 41 (7) ◽  
pp. 1923-1932 ◽  
Author(s):  
Prem Shankar Kumar ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Swarup Kumar Laha
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Rolling Element Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Warping ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Dynamic Time

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

Sign in / Sign up

Export Citation Format

Share Document