A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions

2013 ◽  
Vol 38 (1) ◽  
pp. 23-35 ◽  
Author(s):  
P. Borghesani ◽  
R. Ricci ◽  
S. Chatterton ◽  
P. Pennacchi
Keyword(s):  
Operating Conditions ◽  
Rolling Element Bearing ◽  
Envelope Analysis ◽  
Bearing Diagnostics ◽  
Rolling Element ◽  
Element Bearing ◽  
Variable Operating Conditions

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 Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB

2020 ◽  
Vol 10 (20) ◽  
pp. 7302
Author(s):  
Seokgoo Kim ◽  
Dawn An ◽  
Joo-Ho Choi
Keyword(s):  
Process Simulation ◽  
Rolling Element Bearing ◽  
Fault Identification ◽  
New Developments ◽  
Envelope Analysis ◽  
Practical Applications ◽  
Spectral Kurtosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Simulation Signal

This paper presents a MATLAB-based tutorial to conduct fault diagnosis of a rolling element bearing. While there have been so many new developments in this field, no studies have addressed the tutorial aspects in this field to help the engineers learn the concept and implement by their own effort. The three most common techniques—the autoregressive model, spectral kurtosis, and envelope analysis—are selected to demonstrate the bearing diagnosis process. Simulation signal is introduced to help understand the characteristics of fault signal and carry out the process toward the fault identification. The techniques are then applied to the two real datasets to demonstrate the practical applications, one made by the authors and the other by the Case Western Reserve University, which is known as a standard reference in testing the diagnostic algorithms.

Envelope Analysis Using the Teager-Kaiser Energy Operator for Condition Monitoring of a Wind Turbine Bearing

2014 ◽  
Vol 564 ◽  
pp. 170-175 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Thomas P. Howard ◽  
R.S. Dwyer-Joyce ◽  
Matthew B. Marshall ◽  
Jack Naumann ◽  
...  
Keyword(s):  
Energy Operator ◽  
Rolling Element Bearing ◽  
Envelope Analysis ◽  
Novel Approach ◽  
Rolling Element ◽  
Element Bearing ◽  
The Hilbert Transform ◽  
Frequency Demodulation ◽  
Signal Processing Methods ◽  
Demodulation Method

Different signal processing methods are applied to experimental data obtained from a rolling element bearing rig in order to perform damage detection. Among these methods the Teager-Kaiser energy operator is also proposed as a more novel approach. This energy operator is an amplitude-frequency demodulation method used in this paper as an alternative to the Hilbert Transform in order to perform envelope analysis on the datasets analysed.

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.

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