Vibration Monitoring and Damage Quantification of Faulty Ball Bearings

2004 ◽  
Author(s):  
F. K. Choy ◽  
J. Zhou ◽  
M. J. Braun ◽  
L. Wang
Keyword(s):  
Vibration Monitoring ◽  
Ball Bearings ◽  
Rolling Element Bearings ◽  
Premature Failure ◽  
Time Frequency ◽  
Damage And Failure ◽  
Rolling Element ◽  
Vibration Signature ◽  
On Line ◽  
Bearing Damage

More often then not, the rolling element bearings of rotating machinery are the mechanical components that are first prone to premature failure. Early warning of an impending bearing failure is vital to the safety and reliability of high-speed turbo-machinery. Presently, vibration monitoring is one of the most applied procedures in on-line damage and failure monitoring of rolling element bearings. This paper presents results from an experimental rotor-bearing test rig where quantified damage was induced in the supporting tapered ball bearings. Subsequently the vibration signature due to damage at the inner race of the bearing is examined. Four on-line vibration signature analyzing schemes are used concomitantly: (i) time averaging, (ii) frequency domain analysis, (iii) joint time-frequency analysis (Wigner-Ville and wavelet transforms) and (iv) chaotic vibration analysis (modified Poincare diagrams). The size/level of the damage is corroborated with the vibration amplitude and the resulting relationships are linearized to provide quantification criteria for bearing progressive failure prediction. The results from the above mentioned methodologies are compared for accuracy and redundancy, thus increasing the reliability for early detection of bearing damage and failure. It is shown that the use of the modified Poincare map can provide an effective way for identification and quantification of bearing damage in rolling element bearings.

Vibration Monitoring and Damage Quantification of Faulty Ball Bearings

2005 ◽  
Vol 127 (4) ◽  
pp. 776-783 ◽  
Author(s):  
F. K. Choy ◽  
J. Zhou ◽  
M. J. Braun ◽  
L. Wang
Keyword(s):  
High Speed ◽  
Progressive Failure ◽  
Frequency Domain Analysis ◽  
Vibration Monitoring ◽  
Ball Bearings ◽  
Rolling Element Bearings ◽  
Premature Failure ◽  
Damage And Failure ◽  
Rolling Element ◽  
Safety And Reliability

More often than not, the rolling element bearings in rotating machinery are the mechanical components that are first prone to premature failure. Early warning of an impending bearing failure is vital to the safety and reliability of high-speed turbomachinery. Presently, vibration monitoring is one of the most applied procedures in on-line damage and failure monitoring of rolling element bearings. This paper presents results from an experimental rotor-bearing test rig with quantified damage induced in the supporting rolling element bearings. Both good and damaged radial and tapered ball bearings are used in this study. The vibration signatures due to damage at the ball elements and the inner race of the bearing are also examined. Vibration signature analyzing schemes such as frequency domain analysis, and chaotic vibration analysis (modified Poincare diagrams) are applied and their effectiveness in pinpoint damage are compared in this study. The size/level of the damage is corroborated with the vibration amplitudes to provide quantification criteria for bearing progressive failure prediction. Based on the results from this study, it is shown that the use of the modified Poincare map, based on the relative carrier speed, can provide an effective way for identification and quantification of bearing damage in rolling element bearings.

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

scholarly journals New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiaoming Xue ◽  
Nan Zhang ◽  
Suqun Cao ◽  
Wei Jiang ◽  
Jianzhong Zhou ◽  
...  
Keyword(s):  
Joint Distribution ◽  
Operating Conditions ◽  
Variable Load ◽  
Rolling Element Bearings ◽  
Time Frequency ◽  
Identification Method ◽  
State Identification ◽  
Rolling Element ◽  
Load Conditions ◽  
Testing Set

Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

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.

Bearing Signature Analysis as a Medium for Fault Detection: A Review

2007 ◽  
Vol 130 (1) ◽  
Author(s):  
M. S. Patil ◽  
Jose Mathew ◽  
P. K. RajendraKumar
Keyword(s):  
Fault Detection ◽  
Vibration Measurement ◽  
Signature Analysis ◽  
Rolling Element Bearings ◽  
Bearing Defects ◽  
Rolling Element ◽  
Vibration Signature ◽  
Recent Trends ◽  
Bearing Vibration ◽  
Detection And Diagnosis

Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for detection and diagnosis of the bearing defects. This paper is intended as a tutorial overview of bearing vibration signature analysis as a medium for fault detection. An explanation for the causes for the defects is discussed. Vibration measurement in both time domain and frequency domain is presented. Recent trends in research on the detection of the defects in bearings have been included.

Ceramics in Rolling Element Bearings

1979 ◽  
Author(s):  
C. F. Bersch ◽  
Philip Weinberg
Keyword(s):  
Fatigue Life ◽  
Silicon Nitride ◽  
Cooling System ◽  
Extreme Environment ◽  
Ball Bearings ◽  
Rolling Element Bearings ◽  
Turbine Engine ◽  
Rolling Element ◽  
Rolling Elements ◽  
History Of

The feasibility of using hot-pressed silicon nitride (HPSN) for rolling elements and for races in ball bearings and roller bearings has been explored. HPSN offers opportunities to alleviate many current bearing problems including DN and fatigue life limitations, lubricant and cooling system deficiencies, and extreme environment demands. The history of ceramic bearings and the results of various element tests, bearing tests in rigs, and bearing tests in a turbine engine will be reviewed. The advantages and problems associated with the use of HPSN in rolling element bearings will be discussed.

A PEAK ENERGY CRITERION (P. E.) FOR THE SELECTION OF RESONANCE BANDS IN COMPLEX SHIFTED MORLET WAVELET (CSMW) BASED DEMODULATION OF DEFECTIVE ROLLING ELEMENT BEARINGS VIBRATION RESPONSE

2009 ◽  
Vol 07 (04) ◽  
pp. 387-410 ◽  
Author(s):  
KONSTANTINOS C. GRYLLIAS ◽  
IOANNIS ANTONIADIS
Keyword(s):  
Energy Criterion ◽  
Vibration Response ◽  
Frequency Resolution ◽  
Center Frequency ◽  
Index Number ◽  
Rolling Element Bearings ◽  
Time Frequency ◽  
Peak Energy ◽  
Rolling Element ◽  
Selection Of

Complex Shifted Morlet Wavelets (CSMW) present a number of advantages when used for the demodulation of the vibration response of defective rolling element bearings: (A) They present the optimally located window simultaneously in the time and in the frequency domains; (B) They allow for the maximal time-frequency resolution; (C) The magnitudes of the complex wavelet coefficients in the time domain lead directly to the required envelope; (D) They allow for the optimal selection of both the center frequency and the bandwidth of the requested filter. A Peak Energy criterion (P. E.) is proposed in this paper for the simultaneous automatic selection of both the center frequency and the bandwidth of the relevant wavelet window to be used. As shown in a number of application cases, this criterion presents a more effective behavior than other criteria used (Crest Factor, Kurtosis, Smoothness Index, Number of Peaks), since it combines the advantages of energy based criteria, with criteria characterizing the spikiness of the response.

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