rolling element bearing fault feature extraction using

The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management In this paper a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault Dec 20 2014Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault the time-frequency analysis is often applied to describe the local information of these unstable signals smartly However it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers

Early fault detection and diagnosis in bearings based on

Rolling-element bearings are among the most common components of rotating the field of vibration signal analys is and feature extraction for bearing FDD [5 6] A precise classification [13] the one for detection and the other two for diagnosis of bearing faults Using this new approach the overall complexity of FDD is decreased and at

Feb 27 2017Rolling element bearings are widely used in a variety of rotating machineries If the rolling bearing elements are damaged a cyclical impact transient signal and the vibration signal modulation phenomenon appears when the fault surface contacts other components of the rolling element bearing To demodulate the cyclical impact signal and extract the bearing fault information

Abstract: Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems Traditionally time-domain vibration-based fault diagnosis has some deficiencies such as complex computation of feature vectors excessive dependence on prior knowledge and diagnostic expertise and limited capacity for learning complex relationships in fault

Dec 30 2016After fault feature extraction a fault pattern recognition technique is used to achieve automatically the rolling element bearing fault diagnosis The study on gray relation theory is the foundation of gray system theory which is based on the basic theory of space mathematics to calculate relation coefficient and relation degree between

By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system In response to this problem this paper proposes a new approach based on variational mode decomposition (VMD) SVM and statistical characteristics such as

A Feature Extraction Method Based on Information Theory

these bearing faults are difficult to detect using either of the other techniques it was compared to Keywords: Feature extraction Information theory Reciprocating Machinery Fault diagnosis Rolling element bearing Envelope Analysis OPEN ACCESS

This paper has proposed a new fault feature extraction method for rolling element bearings that can achieve good result by using multi-scale ACDIF and FWEO The ACDIF is used to extract positive and negative impacts existing in bearing vibration signal and the H-PSO-SCAC can deal with the problem of choosing the optimal weight coefficients

Sep 19 2015Extracting reliable features from vibration signals is a key problem in machinery fault recognition This study proposes a novel sparse wavelet reconstruction residual (SWRR) feature for rolling element bearing diagnosis based on wavelet packet transform (WPT) and sparse representation theory WPT has obtained huge success in machine fault diagnosis which demonstrates its potential

Information about the open-access article 'Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions' in DOAJ DOAJ is an online directory that indexes and provides access to quality open access peer-reviewed journals

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault the time-frequency analysis is often applied to describe the local information of these unstable signals smartly However it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers

After fault feature extraction a pattern recognition technique is required to achieve the rolling element bearing fault diagnosis automatically Nowadays a variety of pattern recognition methods have been used in mechanical fault diagnosis of which the most widely used are the support vector machines (SVMs) [ 21 ] and artificial neural

Design/methodology/approach – Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification

Rolling element bearings are the backbone of almost all the rotating machinery Studies show that around 40% of the fail-ures in rotating machines are due to bearing faults 1 If the defect severity is diagnosed well in advance bearing failure and thus machinery shutdowns can be reduced significantly by avoiding catastrophic failure

Fault detection for rolling element bearing using an

The simulation and experimental results on rolling bearing fault illustrate that the proposed EMHPF method is capable of enhancing fault detection of rolling bearing and its feature extraction capability is superior to that of some existing morphological filter methods

rolling bearing even when there is a little fault Key-Words: Phase Space Reconstruction Manifold Learning SLLE LLE Rolling Bearing Fault Diagnosis Feature Extraction 1 Introduction During rolling bearing fault diagnosis because vibration signals contain a wealth of information bearing states can be effectively identified and

Zhang YZ Xu GH Liang L and Wang J 2009 Feature extraction methods for fault classification of rolling element bearing based on nonlinear dimensionality reduction and SVMs Artidcial Intelligence and Computational Intelligence AICI International Conference on 3 228-234 Google Scholar

Design/methodology/approach – Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification

For the rotating machinery system it is a challenge to explore fault detection and diagnosis for multiple-faults condition which simultaneously contains faulty bearing components and faulty gear components In the study a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD) Hilbert transform

The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management In this paper a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault

Wavelet transform has been widely used for the vibration signal based rolling element bearing fault detection However the problem of aliasing inhering in discrete wavelet transform restricts its further application in this field To overcome this deficiency a novel fault detection method for roll element bearing using redundant second generation wavelet packet transform (RSGWPT) is proposed

The Proposed Method for Rolling Element Bearing Fault Feature Extraction: In a bearing-gearbox union system both the rolling element bearing and the gearbox's vibration signals are modulated It is assumed that the impulses generated by the faulty bearing and the faulty gearbox are not the same and there is a difference between the

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault the time-frequency analysis is often applied to describe the local information of these unstable signals smartly However it is difficult to classify the high dimensional feature matrix directly because of too large dimensions for many classifiers