scholarly journals Fault identification and severity analysis of rolling element bearings using phase space topology

2020 ◽  
pp. 107754632092629
Author(s):  
T Haj Mohamad ◽  
C Nataraj
Keyword(s):  
Phase Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Superior Performance ◽  
Space Topology ◽  
Bearing Diagnostics ◽  
Bearing Defects ◽  
Rolling Element ◽  
Element Bearing ◽  
Phase Space Topology

This article presents the application of phase space topology and time-domain statistical features for rolling element bearing diagnostics in rotating machines under variable operating conditions. The results indicate very promising performance in identifying various faults with virtually perfect accuracy, recall, and precision. A comparison with the envelope analysis method is performed to show the superior performance of the proposed approach. In addition, the results demonstrate an outstanding prediction rate for the fault diameter of bearing defects.

scholarly journals Rolling Element Bearing Diagnostics Using Extended Phase Space Topology

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
T. Haj Mohamad ◽  
M. Samadani ◽  
C. Nataraj
Keyword(s):  
Phase Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
System Response ◽  
Operating Speed ◽  
Extended Phase Space ◽  
Extended Phase ◽  
Space Topology ◽  
Rolling Element ◽  
Phase Space Topology

This paper introduces a novel method called extended phase space topology (EPST) for machinery diagnostics and pattern recognition. In particular, the research focuses on fault detection and diagnostics of rolling element bearings. The proposed method is based on mapping the vibrational response onto the density space and approximating the density using orthogonal functions. The method has been applied to vibration data of a rotating machine where the data were measured by proximity probes. The method was applied to two operating conditions: constant operating speed and variable operating speed. As will be shown, the proposed feature extraction method has an outstanding capability in characterizing the system response and diagnosing the system. The method is evidently robust to noise, does not depend on expert knowledge about the system, requires no feature ranking or selection, and can easily be applied in an automated process. Finally, a comparison with utilization of statistical features is performed for each operating condition, which demonstrates that the proposed method performs better than the traditional statistical methods.

scholarly journals Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Weigang Wen ◽  
Zhaoyan Fan ◽  
Donald Karg ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Fractal Dimensions ◽  
Feature Space ◽  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Vibration Signals ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

Advanced Rolling-Element Bearing Diagnostics Based on Cyclic Spectral Analysis

2007 ◽  
Vol 347 ◽  
pp. 265-270
Author(s):  
Jerome Antoni ◽  
Roger Boustany
Keyword(s):  
Background Noise ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Spectral Signatures ◽  
Bearing Diagnostics ◽  
Rolling Element ◽  
Cyclical Behaviour ◽  
Element Bearing ◽  
Incipient Fault ◽  
Inner Race

Rolling-element bearing vibrations are random cyclostationary, that is they exhibit a cyclical behaviour of their statistical properties while the machine is operating. This property is so symptomatic when an incipient fault develops that it can be efficiently exploited for diagnostics. This paper gives a synthetic but comprehensive discussion about this issue. First, the cyclostationarity of bearing signals is proved from a simple phenomenological model. Once this property is established, the question is then addressed of which spectral quantity can adequately characterise such vibration signals. In this respect, the cyclic coherence - and its multi-dimensional extension in the case of multi-sensors measurements -- is shown to be twice optimal: first to evidence the presence of a fault in high levels of background noise, and second to return a relative measure of its severity. These advantages make it an appealing candidate to be used in adverse industrial environments. The use and interpretation of the proposed tool are then illustrated on actual industrial measurements, and a special attention is paid to describe the typical "cyclic spectral signatures" of inner race, outer race, and rolling-element faults.

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