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.

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.

Wavelet Based Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings

2001 ◽  
Author(s):  
Christos T. Yiakopoulos ◽  
Ioannis A. Antoniadis
Keyword(s):  
Vibration Response ◽  
Discrete Wavelet ◽  
Successful Implementation ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Envelope Detection ◽  
Rolling Element ◽  
Time Frequency Localization ◽  
Bearing Vibration ◽  
Demodulation Method

Abstract Envelope detection or demodulation methods for bearing vibration response signals have been established as a dominant analysis method for bearing fault diagnosis, since they can separate the useful part of the signal from its redundant contents. A new effective demodulation method is proposed, based on the Discrete Wavelet Transform (DWT). The method fully exploits the underlying physical concepts of the modulation mechanism, present in the vibration response of faulty bearings, using the excellent time-frequency localization properties of the wavelet analysis. Two key elements, resulting to the successful implementation of the method, are its practical independence from the choice of the specific wavelet family to be used and the limited number of the wavelet levels, that are required for its practical application. Experimental results and industrial measurements for three different types of bearing faults, confirm the validity of the overall approach.

An Intelligent Real Time Design Methodology for Component Selection: An Approach to Managing Uncertainty

1994 ◽  
Vol 116 (4) ◽  
pp. 980-988 ◽  
Author(s):  
S. R. Bradley ◽  
A. M. Agogino
Keyword(s):  
Real Time ◽  
Design Methodology ◽  
Limited Resources ◽  
Rolling Element Bearings ◽  
Component Selection ◽  
Rolling Element ◽  
Improved Design ◽  
The Cost ◽  
Component Database ◽  
Selection Of

An Intelligent Real Time Design (IRTD) methodology is presented for component selection applications under the reality of uncertain and incomplete information. A decision analytic approach is developed with the goal of assisting designers in making decisions that balance the cost of the limited resources consumed during the design process, such as the designer’s time, against the benefit to be derived from the utilization of those resources in terms of expectations of an improved design. This approach is shown to complement other formal methods for design based on interval arithmetic and qualitative optimization, and a general methodology is proposed for performing component selection utilizing a combination of these methods. An example application to the selection of rolling element bearings is presented to clarify the methodology and demonstrate its effectiveness in providing guidance to designers when selecting elements from a component database.

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