An Evolving Approach to Fault Detection of Rolling Element Bearings

2021 ◽  
Author(s):  
Mailson Ribeiro Santos ◽  
Luiz Affonso Guedes
Keyword(s):  
Fault Detection ◽  
Rolling Element Bearings ◽  
Rolling Element

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.

scholarly journals A new methodology for fault detection in rolling element bearings using singular spectrum analysis

2018 ◽  
Vol 148 ◽  
pp. 14002 ◽  
Author(s):  
Hussein Al Bugharbee ◽  
Irina Trendafilova
Keyword(s):  
Fault Detection ◽  
Singular Spectrum Analysis ◽  
Feature Space ◽  
Rolling Element Bearings ◽  
Feature Vectors ◽  
Singular Spectrum ◽  
Rolling Element ◽  
Effective Performance ◽  
Commercial Applications ◽  
Signal Vector

This paper proposes a vibration-based methodology for fault detection in rolling element bearings, which is based on pure data analysis via singular spectrum method. The method suggests building a baseline space from feature vectors made of the signals measured in the healthy/baseline bearing condition. The feature vectors are made using the Euclidean norms of the first three PC’s found for the signals measured. Then, the lagged version of any new signal corresponding to a new (possibly faulty) condition is projected onto this baseline feature space in order to assess its similarity to the baseline condition. The category of a new signal vector is determined based on the Mahalanobis distance (MD) of its feature vector to the baseline space. A validation of the methodology is suggested based on the results from an experimental test rig. The results obtained confirm the effective performance of the suggested methodology. It is made of simple steps and is easy to apply with a perspective to make it automatic and suitable for commercial applications.

Envelope order tracking for fault detection in rolling element bearings

2012 ◽  
Vol 331 (25) ◽  
pp. 5644-5654 ◽  
Author(s):  
Yu Guo ◽  
Ting-Wei Liu ◽  
Jing Na ◽  
Rong-Fong Fung
Keyword(s):  
Fault Detection ◽  
Rolling Element Bearings ◽  
Order Tracking ◽  
Rolling Element

Vibration Analysis Based Feature Extraction for Bearing Fault Detection

2012 ◽  
Vol 197 ◽  
pp. 124-128
Author(s):  
Jie Liu ◽  
Chun Sheng Yang ◽  
Qing Feng Lou
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Rolling Element Bearings ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rotary Machine ◽  
Catastrophic Failures ◽  
Rolling Element ◽  
Significant Research ◽  
Recent Developments

Rolling element bearings are widely used in various rotary machines. Most rotary machine failures are attributed to unexpected bearing faults. Accordingly, reliable bearing fault detection is critically needed in industries to prevent these machines’ performance degradation, malfunction, or even catastrophic failures. Feature extraction plays an important role in bearing fault detection and significant research efforts have thus far been devoted to this subject from both academia and industry. This paper intends to provide a brief review of the recent developments in feature extraction for bearing fault detection, and the focus will be placed on the advances in methods for dealing with the nonstationary characteristics of bearing fault signatures.

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