Diagnostics of rolling bearings for auxiliary electromotors of electric locomotive using parametric model and envelope spectrum (original) (raw)
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International Journa l of Engineering &Technology (IJET), 2018
Purpose: To improve the performance of vibration spectral methods in identification of bearing element faults of freight car axle-boxes. Approach: An algorithm for simulating the expected vibration signal of outer race bearing was implemented. The autoregressive filter and minimum empirical deconvolution method was applied to identify the ball pass outer-race fault frequency and its harmonics on the envelope spectra and squared envelope spectra which were extracted in the proper frequency band. Results: The simulated vibration signal of a faulty bearing shows suitability of the autoregressive filter and minimum empirical deconvo-lution method, envelope and squared envelope spectra for outer race fault identification. There were observed a lower amount of features and their impulse sharpness in outer race faults in the bearing test rig than on the spectra in the wheelset test rig. Conclusions: The deterministic components are removed in the residual signal after using the AR filter and the impulse and noise components that decrease the kurtosis value remain in it. The MED technique additionally enhances the magnitude of increased BPFO components after using the AR filter and, together with it, provides satisfied performance and increases the efficiency of vibration diagnostics.
Envelope Spectrum Analysis with Modified EMD for Fault Diagnosis of Rolling Element Bearing
2020
Selection of demodulation resonant frequency band for envelope analysis is often made by spectrum examination of all frequency band during the fault diagnosis process in time-frequency analysis. To overcome this limitation, a new criterion to select a suitable resonant frequency band for concern bearing defect frequencies has been examined experimentally in present work. Synchronized resonant frequency band is obtained based on orthogonal reverse biorthogonal wavelet RBIO 5.5 for decomposition of signal, using wavelet packet transform for time-frequency analysis. Concept of pseudo-IMF has not been explained by earlier researchers and the authors are interested to study the effectiveness of subsequent IMF with different resonant frequency band. The experimental results from set up indicate that the present concept is a validated tool, to develop an efficient online fault diagnosis system to diagnose the incipient bearing faults.
Vibration analysis for fault diagnosis of rolling element bearings
2014
Abstract: Bearing failure is often attributed to be one of the major causes of breakdown in industrial rotating machines that operate at high and low speeds. In this work we have used some of the modern techniques of vibration analysis included today in some commercial vibration analyzers. For the experimental study, good shape ball bearings and localized defect in the outer race ball bearings, were tested under different levels of fault severity and various load and speed conditions. Normal spectral analysis, demodulation, PeakVue and real zoom analysis were the techniques used for the analysis. [Ebrahim Ebrahimi. Vibration analysis for fault diagnosis of rolling element bearings..Journal of American
International Journal of Mechanical Engineering, 2021
Almost all machines having rotating parts contain rolling element bearings to support the rotating parts during power transmission. Bearing failure is a major cause of the breakdown of machines. Hence it is necessary to identify the defects and their severity in their early stage to avoid breakdown of the machine and catastrophic damages. Defective bearings generation vibrations and various vibration signal analysis techniques have been developed by researchers for bearing condition monitoring. This paper presents an introduction and updated review of vibration signal analysis techniques used for the detection of defects in rolling element bearings. In this paper, vibration signal analysis techniques used for bearing defect detection are reviewed according to their classification viz. time domain, frequency domain, and time-frequency domain. This study will help the researchers to understand recent developments in the detection of defects of bearings from their vibration signals.
Fault Diagnosis in Rolling Element Bearing Using Filtered Vibration and Acoustic Signal
2018
The defects present in the rolling element bearings may affect the performance of a machinery and may reduces its efficiency. So early detection of the faults in the rolling element bearings is very essential. The vibration or acoustic signature generated from the rolling element bearings may be used as the measuring parameters for the fault diagnosis. The rolling element bearing has its own signature(vibration and acoustic signal) in its healthy condition and when a defect occurs in it then its vibration and acoustic signatures get changed. The vibration and acoustic signatures in healthy and defective conditions are compared in time, frequency and timefrequency domain to detect the fault. Though either vibration or acoustic signature alone can be used for the fault detection but in this work both the vibration and acoustic signatures are used for the fault detection in the bearings. In this work initially the statistical analysis is done on the acquired vibration and acoustic sign...
FAULT DETECTION IN ROLLING ELEMENT BEARINGS USING VIBRATION AND ACOUSTIC EMISSION SIGNALS
A study is presented for fault detection in rolling element bearings using vibration and acoustic emission (AE) signals. The acquired signals of a rotating machine with normal and defective bearings are analysed using different signal processing techniques. The features obtained from the original and the processed signals are used for detection of bearing condition. The features include statistical, spectral, cepstral and time-spectral parameters of the acquired and the preprocessed signals. The procedure is illustrated through the experimental vibro-acoustic signals of a rotating machine for different types of bearing faults. Several signal processing techniques with both vibration and AE signals are considered. The results present a comparative study of the signals and the signal processing techniques for detection of different types of faults in rolling element bearings.