Rolling Element Fault Research Articles

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

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.

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis

This paper presents a novel signal processing scheme, diagonal slice spectrum assisted optimal scale morphological filter (DSS-OSMF), for rolling element fault diagnosis. In this scheme, the concept of quadratic frequency coupling (QFC) is firstly defined and the ability of diagonal slice spectrum (DSS) in detection QFC is derived. The DSS-OSMF possesses the merits of depressing noise and detecting QFC. It can remove fault independent frequency components and give a clear representation of fault symptoms. A simulated vibration signal and experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearing. In addition, comparisons are performed between a multi-scale morphological filter (MMF) and a DSS-OSMF. DSS-OSMF outperforms MMF in detection of an outer race fault and a rolling element fault of a rolling element bearing.

Feature extraction of rolling element bearing’ compound faults based on cyclic wiener filter with constructed reference signals

Feature extraction of rolling element bearing’s compound faults is a challenging task due to the complexity and the mutual coupling phenomenon among the kinds of faults. A new method based on cyclic wiener filter with constructed reference signals is proposed in the paper. The reference signals of the rolling element bearing’ inner race fault, outer race fault and rolling element fault are created respectively based on the rolling element bearing’ theoretical fault frequencies. Here, the created signals are used as the expected responses. Then the observed compound faults signal and the constructed reference signal are input into the cyclic wiener filter together. At last, the envelope demodulation method is applied on the filtered signals respectively and satisfactory fault feature extraction results are obtained. The effectiveness of the proposed method is verified through simulation. Furthermore, the advantages of the proposed method over other signal handling method such as spectral kurtosis (SK) are verified through experiment.

Amplitude and frequency demodulation analysis for fault diagnosis of planet bearings

The vibration signals of planet bearings feature more complex amplitude modulation and frequency modulation (AM-FM) than those of fixed-axis bearings, due to the effects of time-varying vibration transfer path, load zone passing and time-varying angles between gear pair mesh lines of action and impact force vector, in addition to that of bearing fault. Their Fourier spectra have a complex sideband structure, leading to difficulty in feature extraction and fault diagnosis. In order to address this issue, a joint amplitude and frequency demodulation analysis method is proposed to reveal the fault features, by considering the modulation characteristics. To thoroughly understand planet bearing vibration characteristics, the explicit equations for amplitude and frequency demodulated spectra of outer race, rolling element and inner race fault cases are derived, and the fault symptoms are summarized respectively. The signal is firstly decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), thus meeting the mono-component requirement by instantaneous frequency calculation. Then a sensitive component with the instantaneous frequency fluctuating around the resonance frequency is chosen for further frequency demodulation analysis. Finally, planet bearing fault can be diagnosed by matching the peaks identified in amplitude and frequency demodulated spectra with the theoretical fault characteristic frequencies. The proposed method is validated with both numerical simulated and lab experimental signal analyses.

Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery

Condition monitoring of rotating machinery is important to promptly detect early faults, identify potential problems, and prevent complete failure. Four direct classification methods were introduced to diagnose the regular condition, inner race defect, outer race defect, and rolling element defect of rolling bearings. These include the K-Nearest Neighbor algorithm (KNN), Probabilistic Neural Network (PNN), Particle Swarm Optimization optimized Support Vector Machine (PSO-SVM) and a Rule-Based Method (RBM) based on the MLEM2 algorithm and a new Rule Reasoning Mechanism (RRM). All of them can be run on the Fault Decision Table (FDT) containing numerical variables and output fault categories directly. The diagnosis results were discussed in terms of accuracy, time consumption, intelligibility, and maintainability. Especially, the interactions of the systems and human experts were compared in detail. It was concluded that all the four methods can work satisfactorily on accuracy, in an order of the PSO-SVM ranking the first, followed by the RBM that functioned the friendliest. Moreover, the RBM had the ability of feature reduction by itself, and would be most suitable for real-time applications.

A Hybrid Feature Selection Scheme for Reducing Diagnostic Performance Deterioration Caused by Outliers in Data-Driven Diagnostics

In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.

Vibration signal models for fault diagnosis of planet bearings

Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

Simulation of vibration signal generated by a defective rolling element bearing

With the advent of vibration-based fault detection techniques a lot of work has been done to identify flaws of rolling element bearing through the processing of simulated and experimental vibration signals. This paper is aimed to describe the methodology of developing a simulated signal of a defective rolling element bearing which can replicate the experimental vibration signal emanated under similar condition. Various factors like change in bearing rotational speed, radial load, defect width, pseudo periodicity and bearing parameters have been considered to generate simulated signals. Components of characteristic defect frequencies obtained through simulated signals have been compared with the experimental results of previous researchers wherein a fair resemblance in patterns authenticates the generation of simulated signals through proposed methodology.

Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources.In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data11This article includes complete dataset as supplementary material (accessible from online version). from a laboratory setup to validate the diagnosis tool for bearing raceway and rolling element faults.

Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter

Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, respectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.

Simulation-based approach for detection of bearing defects through continuous wavelet transform

Rolling element bearings are the heart of modern industrial machineries, thus, an early detection of budding faults in bearings is essential to avoid ruinous machine failures. This study shows a method to examine the vibration signals of defective rolling element bearings in noisy environments. Both simulated and experimental signals of an identical bearing have been used to investigate outer race defect. A transfer function has been developed on the basis of bearing specifications. Moreover, continuous wavelet transformation (CWT) using frequency B spline wavelet has also been used to predict the defect in the rolling element bearing. Selection of spline wavelet and effect of parameters such as order of spline, bandwidth and central frequency, on its shape has been discussed. Correlation in the results obtained through CWT of simulated and experimental signals authenticates the use of proposed methodology in easier fault detection in bearings.

Enhanced Detection of Bearing Faults Based on Adaptive Multi-scale Self-complementary Top-Hat Transformation

Aiming at the difficulty of extracting impulsive fault feature of rolling element bearings in practical engineering,a novel method named adaptive multi-scale self-complementary Top-Hat(AMSTP) transformation is proposed to enhance detection of bearing faults. It can enhance the impulsiveness of the bearing fault vibration signal and depress strong background noise,and constructing multi-scale is better to depress noise and retain detail of signal. The most optimal structure element(SE) scale is selected by using a novel method of feature amplitude energy radio(FAER),and it is applied in detecting fault feature of impulsive signal successfully. The performance of the proposed method is validated by both simulated signal and vibration signals of defective rolling element bearings with ball and inner faults. In addition the method could achieve better effect on feature extraction and have more operation efficiency than open-closing and close-opening combined morphological method based on signal noise ratio(SNR) criterion.

A nonlinear dynamic vibration model of defective bearings – The importance of modelling the finite size of rolling elements

This paper presents an improved nonlinear dynamic model of the contact forces and vibration response generated in defective rolling element bearings. The improvement comes about by considering the finite size of the rolling elements which overcomes the limitations exhibited by previous models caused by the modelling of rolling elements as point masses. For line spall defects, a low frequency event occurs in the measured vibration response when a rolling element enters the defect. Previous models were not able to accurately predict this event without making the simulated and actual defect geometries significantly different. Comparisons between the proposed model, previous models and experimental results are carried out to show that the low and high frequency events are more accurately predicted. This analysis identifies and explains the mechanisms leading to inaccuracy of the previous models. The model developed here can be used to aid in the development of new diagnostic algorithms.

A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition

Defective rolling bearing response is often characterized by the presence of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

Analyses of contact forces and vibration response for a defective rolling element bearing using an explicit dynamics finite element model

This paper provides insights into the physical mechanism by which defect-related impulsive forces, and consequently, vibrations are generated in defective rolling element bearings. A dynamic nonlinear finite element model of a rolling element bearing with an outer raceway defect was numerically solved using the explicit dynamics finite element software package, LS-DYNA. A hypothesis was developed to explain the numerical noise observed in the predicted vibrations and contact forces, and the noise frequencies were analytically estimated. In-depth analyses of the numerically estimated dynamic contact forces between the rolling elements and the raceways of a bearing, which are not measured in practice, and have not been reported previously, are presented in this paper. Several events associated with the traverse of the rolling elements through the outer raceway defect are elaborated, and the impulsive force generating mechanism is explained. It was found that the re-stressing of the rolling elements that occurs near the end of a raceway defect generates a burst of multiple short-duration force impulses. The modelling results also highlight that much higher contact forces and accelerations are generated on the exit of the rolling elements out of defect compared to when they strike the defective surface. A bearing with a machined outer raceway defect was tested in a controlled experiment; the measured acceleration response compared favourably with the numerically modelled acceleration results, thereby, validating the low- and high-frequency characteristics of the de-stressing and re-stressing of the rolling elements, respectively.

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration signals emanated from defective rolling element bearings

Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.

Bearing fault severity estimation using time-based descriptors for rotating electric machines

This paper evaluates the bearing fault severity in rotating electric machines based on the time-based descriptors derived from machine vibration signal. Time-based descriptors are independent of bearing dimensions and machine dynamics. To establish the relationship between the time-based descriptor and bearing fault severity, a wide range of defective bearings are examined on an experimental set-up and the factors contributing to the value of time-based descriptor are determined. The application of this technique to the bearing faults such as rolling element, inner-race and outer-race fault, has confirmed that time-based descriptor successfully measures the severity of faults. The significance of time-based descriptors has been evaluated and tested by developing online bearing failure situation via shaft current. Since the time-based descriptors are independent of dimensions and characteristic frequencies of the bearing, the severity estimation is less sensitive to operating conditions for data acquisition. The laboratory investigations on 10-hp cage induction motor are presented.