Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

2019 ◽  
Vol 233 (5) ◽  
pp. 868-880 ◽  
Author(s):  
Yao Cheng ◽  
Dong Zou
Keyword(s):  
Decomposition Method ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Local Means ◽  
Residual Noise ◽  
Rolling Element ◽  
Noisy Signals ◽  
Mode Mixing

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

scholarly journals Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time-Varying Speed Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Guang-Quan Hou ◽  
Chang-Myung Lee
Keyword(s):  
Research Work ◽  
Defect Size ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Time Varying ◽  
Entry And Exit ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

Fault diagnosis and failure prognostics for rolling element bearing are helpful for preventing equipment failure and predicting the remaining useful life (RUL) to avoid catastrophic failure. Spall size is an important fault feature for RUL prediction, and most research work has focused on estimating the fault size under constant speed conditions. However, estimation of the defect width under time-varying speed conditions is still a challenge. In this paper, a method is proposed to solve this problem. To enhance the entry and exit events, the edited cepstrum is used to remove the determined components. The preprocessed signal is resampled from the time domain to the angular domain to eliminate the effect of speed variation and measure the defect size of a rolling element bearing on outer race. Next, the transient impulse components are extracted by local mean decomposition. The entry and exit points when the roller passes over the defect width on the outer race were identified by further processing the extracted signal with time-frequency analysis based on the continuous wavelet transform. The defect size can be calculated with the angle duration, which is measured from the identified entry and exit points. The proposed method was validated experimentally.

Feature Selection for Fault Detection in Rolling Element Bearings Using Mutual Information

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Karthik Kappaganthu ◽  
C. Nataraj
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Quantitative Measure ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Degree Of Confidence ◽  
Hidden Layer ◽  
Optimal Feature

Rolling element bearings are among the key components in many rotating machineries. It is hence necessary to determine the condition of the bearing with a reasonable degree of confidence. Many techniques have been developed for bearing fault detection. Each of these techniques has its own strengths and weaknesses. In this paper, various features are compared for detecting inner and outer race defects in rolling element bearings. Mutual information between the feature and the defect is used as a quantitative measure of quality. Various time, frequency, and time-frequency domain features are compared and ranked according to their cumulative mutual information content, and an optimal feature set is determined for bearing classification. The performance of this optimal feature set is evaluated using an artificial neural network with one hidden layer. An overall classification accuracy of 97% was obtained over a range of rotating speeds.

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.

scholarly journals Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Haibin Wang ◽  
Junbo Long
Keyword(s):  
Lower Order ◽  
Signal Analysis ◽  
Stable Distribution ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
S Transform ◽  
Synchrosqueezing Transform ◽  
Better Than ◽  
Low Order

Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment. Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST). In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST. The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction. Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal. Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.

scholarly journals Identification of Bearing Faults using Wavelet Transform

2019 ◽  
Vol 8 (4) ◽  
pp. 9829-9833
Keyword(s):  
Condition Monitoring ◽  
Specific Problem ◽  
Wavelet Packet ◽  
Outer Race ◽  
Time Frequency ◽  
Machine Element ◽  
Rolling Element ◽  
Increasing Demand ◽  
Application Specific ◽  
Inner Race

This research is concerned with description of a scheme for bearing’s localized defect detection based on wavelet packet transform (WPT). WPT provides a high resolution time-frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element on the defect, are detected. The objective of this work is to emphasis on the outer race defect, inner race defect and ball defect. In modern industrial scenario, there is increasing demand for automatic condition monitoring that reduce the gap between digital model and actual product. With reliable condition monitoring, faults such as machine element failures could be identified in their early-stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

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