New Vision about the Mixed Eccentricity Fault Causes in Induction Motors and its relationship with the Rolling Element Bearing Faults : Analytical model dedicated to the REB faults

2018 ◽  
Author(s):  
Noureddine Bessous ◽  
Ali Chemsa ◽  
Salim Sbaa
Keyword(s):  
Analytical Model ◽  
Induction Motors ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Rolling Element ◽  
Element Bearing ◽  
New Vision

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

2001 ◽  
Vol 123 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Peter W. Tse ◽  
Y. H. Peng ◽  
Richard Yam
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Rolling Element Bearing ◽  
Frequency Spectra ◽  
Bearing Faults ◽  
Time Frequency ◽  
Envelope Detection ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Characteristic

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

scholarly journals Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhipeng Feng ◽  
Fulei Chu
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Cyclic Frequency ◽  
Fm Signals ◽  
Rolling Element ◽  
Element Bearing ◽  
Bearing Vibration ◽  
Cyclic Spectrum

Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique

2009 ◽  
Vol 40 (3-4) ◽  
pp. 393-402 ◽  
Author(s):  
Khalid F. Al-Raheem ◽  
Asok Roy ◽  
K. P. Ramachandran ◽  
D. K. Harrison ◽  
Steven Grainger
Keyword(s):  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Rolling Element ◽  
Element Bearing ◽  
Faults Diagnosis

scholarly journals Analytical model and spectral characteristics of acoustic emission signal produced by localized fault of rolling element bearing

2021 ◽  
Vol 39 (4) ◽  
pp. 831-838
Author(s):  
Fazhong Li ◽  
Zengshui He ◽  
Lin Zhang ◽  
Anbo Ming ◽  
Yongsheng Yang
Keyword(s):  
Acoustic Emission ◽  
Analytical Model ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Emission Signal ◽  
Rolling Element ◽  
Excitation Mechanisms ◽  
Element Bearing

The accurate description of acoustic emission signals produced by the localized fault of a rolling element bearing plays an important role in its feature extraction and analysis. This paper analyzes the excitation mechanisms and develops the analytical model of acoustic emission signals produced when the rolling element bearing passes across the localized fault on the inner or outer race. Based on the analytical model, the spectral characteristics are discussed substantially. Simulations and experiments are carried out to validate the efficacy of the model developed in the paper. The experimental results show that the response signal thus produced has two parts. The first one is produced by the entry of the rolling element bearing, while the other is produced by the departure of the rolling element bearing. The energy of both parts is concentrated around the resonance frequency of the acoustic emission transducer. Generally, the interval of adjacent acoustic emission events is not equivalent to each other and the corresponding spectrum is continuous in the high frequency band.

Iterative asymmetric multiscale morphology and its application to fault detection for rolling element bearing

2017 ◽  
Vol 232 (2) ◽  
pp. 316-330 ◽  
Author(s):  
Tingkai Gong ◽  
Yanbin Yuan ◽  
Xiaohui Yuan ◽  
Xiyang Wang ◽  
Xiaotao Wu ◽  
...  
Keyword(s):  
Simulation Experiment ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Structuring Element ◽  
Rolling Element ◽  
Difference Filter ◽  
Element Bearing ◽  
Stop Condition ◽  
Low Amplitude ◽  
Inner Race

The impulsive signals produced by bearing faults are usually modulated in amplitude. Multiscale morphology is suited to demodulate the signal because of its powerful demodulation ability. However, when the structuring element scales are increased gradually, the multiscale morphology method using closing and/or opening allows the low-amplitude impulses to be eliminated. Therefore, iterative asymmetric multiscale morphology is explored in this paper to handle the problem. Firstly, a modified difference filter is developed based on closing and opening to conduct iterative morphology operation, and then a type of asymmetric-multiscale is designed to set the structuring element scales of the modified difference filter filter for demodulating the fault signal with amplitude modulation well. Meanwhile, iterative morphology is conducted to enhance the impulsive features, and kurtosis acts as the iteration stop condition. The effectiveness of the proposed method is evaluated by both simulation experiment and the vibration signals of rolling element bearings with an inner race, an outer, and a rolling element faults. In comparisons with the conventional multiscale morphology, the results demonstrate that the iterative asymmetric multiscale morphology method has better diagnosis for the bearing faults.

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