Damage In Rolling Element Bearings Research Articles

Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition

A Single Ensemble Empirical Mode Decomposition (SEEMD) is proposed to locate the damage in rolling element bearings. The SEEMD method eliminates the need for adding or subtracting noise repeatedly to process signals, unlike other techniques that rely on multiple ensembles. The SEEMD requires a single sifting process of a modified raw signal to reduce the computation time significantly. The other advantage of the SEEMD method is its success in dealing with non-Gaussian or non-stationary perturbing signals. In SEEMD, a fractional Gaussian noise (FGN) is initially added to the raw signal to emphasize the signal's high frequencies. Then, a convoluted white Gaussian noise is multiplied on the resulting signal, changing its spectral content, which helps in extracting the weak periodic signal. Finally, the obtained signal is decomposed using a single sifting process. The proposed methodology is applied to the raw signals obtained from the mining industry. These signals are difficult to analyze since cyclic impulsive components are obscured by noise and other interference. Based on the results, the proposed method can effectively detect the fault where the signal of interest (SOI) has been extracted with good quality.

Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator

Abstract Local damage in rolling element bearings usually generates periodic impulses in vibration signals. The severity, repetition frequency and the fault excited resonance zone by these impulses are the key indicators for diagnosing bearing faults. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing fault excited resonance zone is based on the maximization of a proposed impulse detection measure “Temporal energy operated auto correlated kurtosis”. The proposed indicator is robust and consistent in evaluating the impulsiveness of fault signals in presence of interfering vibration such as heavy background noise or sporadic shocks unrelated to the fault or normal operation. The structure of the proposed indicator enables it to be sensitive to fault severity. For enhanced fault classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.

Envelope pre-processing techniques for rolling element bearing diagnosis in variable speed conditions

In rotating machine diagnosis, the squared envelope spectrum (SES) is one of the most efficient indicators of the presence of cyclostationarity, which is a typical symptom of damage in rolling element bearings. Similarly to other cyclostationary tools, the SES should be applied on the pure random part of the signal, otherwise spurious peaks may appear and hide the cyclostationary content. Therefore, conventional deterministic/random separation (DRS) techniques are usually used as a pre-processing step to suppress the deterministic (periodic) component. In speed-varying regimes, the SES is combined with computed order tracking to obtain an order-domain representation rather than a Hertzian one. Through being order tracked, the deterministic components undergo speed-dependent variations in their magnitude and phase, thus losing their periodicity. This necessarily invalidates the working assumption of conventional DRS techniques and, consequently, jeopardises the efficiency of the SES. Recently, some sophisticated pre-processing techniques have been proposed to deal with this issue, the most important of which are the improved synchronous average (ISA), the cepstrum pre-whitening (CPW) and the generalised synchronous average (GSA). This paper aims to provide a comparative study of these techniques by highlighting their pros and cons. The comparison is performed on real-world signals.

Identification and stochastic modelling of sources in copper ore crusher vibrations

A problem of rolling element bearings diagnostics for different machines is widely discussed in the literature. Most of the methods are based on the vibration signal analysis. However for some real signals the classical methods of damage detection are insufficient because of the specific nature of examined data. This specific nature is very often manifested through overlapping, mixing or interleaving of processes with different statistical properties and may be the result of different sources that have influence on the analysed signal. The problem of different sources identification and parametrisation of processes which are related to them is very challenging and requires advanced techniques. There are many methods which can be useful in this context however each signal should be analysed separately and there is no universal technique adequate to all possible time series. In this paper we propose a method of sources identification for vibration signal from the heavy duty crusher used in mineral processing plant. A crusher is a kind of machine which use a metal surface to crumble materials into small fractional pieces. During this process, as well as during entering material stream into the crusher, a lot of impacts appear. They are present in vibration signal acquired from bearings housing. Moreover, for some cases we also observe cyclic impulses which may be related to damage of rolling element bearings in the machine. The proposed sources identification method, especially useful for crushers vibrations, is based on the statistical analysis of examined data. Moreover by using advanced techniques of time series theory we propose a stochastic model that exhibits similar statistical properties as analysed signals. The introduced technique can be a starting point to damage detection of rolling element bearings of copper ore crushers.

Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the local damage of rolling element bearings. A new method based on ensemble empirical mode decomposition (EEMD) and the Teager energy operator is proposed to extract the characteristic frequency of bearing fault. The signal is firstly decomposed into monocomponents by means of EEMD to satisfy the monocomponent requirement by the Teager energy operator. Then, the intrinsic mode function (IMF) of interest is selected according to its correlation with the original signal and its kurtosis. Next, the Teager energy operator is applied to the selected IMF to detect fault-induced impulses. Finally, Fourier transform is applied to the obtained Teager energy series to identify the repeating frequency of fault-induced periodic impulses and thereby to diagnose bearing faults. The principle of the method is illustrated by the analyses of simulated bearing vibration signals. Its effectiveness in extracting the characteristic frequency of bearing faults, and especially its performance in identifying the symptoms of weak and compound faults, are validated by the experimental signal analyses of both seeded fault experiments and a run-to-failure test. Comparison studies show its better performance than, or complements to, the traditional spectral analysis and the squared envelope spectral analysis methods.

Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings

Localized damage of rolling element bearings generates periodic impulses during running. The repeating frequency of impulses is a key indicator for diagnosing the localized damage of bearings. A new method, called Teager energy spectrum, is proposed to diagnose the faults of rolling element bearings. It exploits the unique advantages of Teager energy operator in detecting transient components in signals to extract periodic impulses of bearing faults, and uses the Fourier spectrum of Teager energy to identify the characteristic frequency of bearing faults. The effectiveness of the proposed method is validated by analyzing the experimental bearing vibration signals.

Time-Wavelet Energy Spectral Analysis for Fault Diagnosis of Rolling Element Bearings

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the localized damage of rolling element bearings. A new method, so called time-wavelet energy spectrum, is proposed to extract the characteristic frequency of faulty elements. It is applied to analyzing the vibration signals of bearings under normal and faulty (with damage on outer race, inner race and ball respectively) statuses. The analysis results show that the time-wavelet energy spectrum is effective in extracting the repeating frequency of periodic impulses. It can not only extract the relatively significant fault feature of outer race damage, but also can extract the weaker fault features of inner race and ball damage.

Vibration Monitoring and Damage Quantification of Faulty Ball Bearings

More often than not, the rolling element bearings in rotating machinery are the mechanical components that are first prone to premature failure. Early warning of an impending bearing failure is vital to the safety and reliability of high-speed turbomachinery. Presently, vibration monitoring is one of the most applied procedures in on-line damage and failure monitoring of rolling element bearings. This paper presents results from an experimental rotor-bearing test rig with quantified damage induced in the supporting rolling element bearings. Both good and damaged radial and tapered ball bearings are used in this study. The vibration signatures due to damage at the ball elements and the inner race of the bearing are also examined. Vibration signature analyzing schemes such as frequency domain analysis, and chaotic vibration analysis (modified Poincare diagrams) are applied and their effectiveness in pinpoint damage are compared in this study. The size/level of the damage is corroborated with the vibration amplitudes to provide quantification criteria for bearing progressive failure prediction. Based on the results from this study, it is shown that the use of the modified Poincare map, based on the relative carrier speed, can provide an effective way for identification and quantification of bearing damage in rolling element bearings.

Diagnostics of rolling-element bearing condition by means of vibration monitoring under operating conditions

Many investigations have been made of the correlation of diagnostic parameters with stress and damage in rolling-element bearings. It was found that parameters normalised to zero conditions are more suitable for diagnostic purposes than time-dependent absolute values. A new superior diagnostic parameter can be simply determined.