Faults In Rolling Element Bearings Research Articles

Study of Vibrational Response of a Rotary System with Defective Rolling Element Bearing

Study of Vibrational Response of a Rotary System with Defective Rolling Element Bearing

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine

Rolling-element bearings (REBs) faults are one of the most common breakdowns of rotating machines, thus proposing effective bearing fault diagnosis and classification methods is vital. In previous studies, lots of bearing fault classification methods have been proposed to solve the problem in low signal-to-noise ratio (SNR) conditions. Though satisfactory classification results have been obtained, in consideration of the practicability and application scenarios, there are still many aspects to improve, such as the complexity of method and the classification ability in lower SNR conditions. Therefore, this paper presents a novel method that combines lower-order moment spectrum with support vector machine (SVM) for bearing fault classification in low SNR conditions. The lower-order moment spectrum reduces influence of Gaussian noise and enhances the quality of fault feature. A bandpass filter group (BPFG) has been used to reduce the dimension of the lower-order moment spectra (LOMS) as feature vectors. And a following SVM has been applied as the fault classifier, due to the mature application and satisfactory performance in fault classification. The proposed method is demonstrated to have strong ability of classification in low SNR conditions experimentally.

An Incorrect Data Detection Method for Big Data Cleaning of Machinery Condition Monitoring

The presence of incorrect data leads to the decrease of condition-monitoring big data quality. As a result, unreliable or misleading results are probably obtained by analyzing these poor-quality data. In this paper, to improve the data quality, an incorrect data detection method based on an improved local outlier factor (LOF) is proposed for data cleaning. First, a sliding window technique is used to divide data into different segments. These segments are considered as different objects and their attributes consist of time-domain statistical features extracted from each segment, such as mean, maximum and peak-to-peak value. Second, a kernel-based LOF (KLOF) is calculated using these attributes to evaluate the degree of each segment being incorrect data. Third, according to these KLOF values and a threshold value, incorrect data are detected. Finally, a simulation of vibration data generated by a defective rolling element bearing and three real cases concerning a fixed-axle gearbox, a wind turbine, and a planetary gearbox are used to verify the effectiveness of the proposed method, respectively. The results demonstrate that the proposed method is able to detect both missing segments and abnormal segments, which are two typical incorrect data, effectively, and thus is helpful for big data cleaning of machinery condition monitoring.

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

Rolling element bearings (REB) are widely used in all walks of life, and they play an important role in the health operation of all kinds of rotating machinery. Therefore, the fault diagnosis of REB has attracted substantial attention. Fault diagnosis methods based on time-frequency signal analysis and intelligent classification are widely used for REB because of their effectiveness. However, there still exist two shortcomings in these fault diagnosis methods: (1) A large amount of redundant information is difficult to identify and delete. (2) Aliasing patterns decrease the methods’ classification accuracy. To overcome these problems, this paper puts forward an improved fault diagnosis method based on tree heuristic feature selection (THFS) and the dependent feature vector combined with rough sets (RS-DFV). In the RS-DFV method, the feature set was optimized through the dependent feature vector (DFV). Furthermore, the DFV revealed the essential difference among different REB faults and improved the accuracy of fault description. Moreover, the rough set was utilized to reasonably describe the aliasing patterns and overcome the problem of abnormal termination in DFV extraction. In addition, a tree heuristic feature selection method (THFS) was devised to delete the redundant information and construct the structure of RS-DFV. Finally, a simulation, four other feature vectors, three other feature selection methods and four other fault diagnosis methods were utilized for the REB fault diagnosis to demonstrate the effectiveness of the RS-DFV method. RS-DFV obtained an effective subset of five features from 100 features, and acquired a very good diagnostic accuracy (100%, 100%, 99.51%, 100%, 99.47%, 100%), which is much higher than all comparative tests. The results indicate that the RS-DFV method could select an appropriate feature set, deeply dig the effectiveness of the features and more exactly describe the aliasing patterns. Consequently, this method performs better in REB fault diagnosis than the original intelligent methods.

Bearing fault analysis using kurtosis and wavelet based multi-scale PCA

The vibration signal monitoring that is being generated by a rotor supported by a rolling element bearing is becoming important to define reliability of rotary machine. It is most prudent and useful method for bearing fault detection. Recently, there has been a lot of research on rolling element bearings fault. The kurtosis is most vital parameter to find inner race fault and outer race fault. It is enhanced by a proper selection of variable frame sizes and decompositions levels using wavelet based multi-scale principal component analysis (WMSPCA). It is observed that the kurtosis changes significantly as compared to the actual kurtosis of the un-decomposed faulty signals by proper selection of frame size and decompositions level.

A Hybrid Time-Frequency Analysis Method for Railway Rolling-Element Bearing Fault Diagnosis

The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

Health Indicator Construction Based on MD-CUMSUM With Multi-Domain Features Selection for Rolling Element Bearing Fault Diagnosis

The initial fault signal of rolling element bearing is extremely weak and could be easily masked by strong background noise. Different features of vibration signal can be different sensitivity to initial fault and performance degradation. Moreover, individual features cannot reflect bearing fault rationally and these features reveal non-monotonic behavior when the bearing condition deteriorates. A Health Indicator (HI) is proposed based on Mahalanobis Distance and Cumulative Sum (MD-CUMSUM). The time-frequency domain features extracted through Singular Value Decomposition based on Variational Mode Decomposition (VMD-SVD) and several optimal time domain features are used to calculate Mahalanobis Distances (MDs). The coarse-to-fine diagnosing strategy is proposed to determine the initial fault of rolling bearing. The obtained HI is utilized to estimate the different performance degradation stages of the bearing depending on the thresholds. This method is verified by utilizing two different experiments. The results demonstrate that the approach has the capability of estimating initial fault and determining degradation stages of bearing.

Nanomaterial-based Lubricant Effect on the Vibrations of Defective Rolling Element Bearings

Nanomaterial-based Lubricant Effect on the Vibrations of Defective Rolling Element Bearings

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions.

Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions.

Rolling Element Bearing Fault Diagnosis by Combining Adaptive Local Iterative Filtering, Modified Fuzzy Entropy and Support Vector Machine.

A new fault feature extraction method for rolling element bearing is put forward in this paper based on the adaptive local iterative filtering (ALIF) algorithm and the modified fuzzy entropy. Due to the bearing vibration signals’ non-stationary and nonlinear characteristics, the ALIF method, which is a new approach for the analysis of the non-stationary signals, is used to decompose the original vibration signals into a series of mode components. Fuzzy entropy (FuzzyEn) is a nonlinear dynamic parameter for measuring the signals’ complexity. However, it only emphasizes the signals’ local characteristics while neglecting its global fluctuation. Considering the global fluctuation of bearing vibration signals will change with the bearing working condition varying, we modified the FuzzyEn. The modified FuzzyEn (MFuzzyEn) of the first few modes obtained by the ALIF is utilized to form the fault feature vectors. Subsequently, the corresponding feature vectors are input into the multi-class SVM classifier to accomplish the bearing fault identification automatically. The experimental analysis demonstrates that the presented ALIF-MFuzzyEn-SVM approach can effectively recognize the different fault categories and different levels of bearing fault severity.

A multi-step progressive fault diagnosis method for rolling element bearing based on energy entropy theory and hybrid ensemble auto-encoder

It is meaningful to efficiently identify the health status of bearing and automatically learn the effective features from the original vibration signals. In this paper, a multi-step progressive method based on energy entropy (EE) theory and hybrid ensemble auto-encoder (HEAE), systematically blending the statistical analysis approach with the deep learning technology, is proposed for rolling element bearing (REB) fault diagnosis. Firstly, a preliminary detection about the REB health status is performed by the statistical analysis technique integrated with the EE theory. Secondly, if fault exists in REB, a new HEAE is constructed based on denoising auto-encoder and contractive auto-encoder to strengthen the feature learning ability and automatically extract the deep state features from the raw data. Subsequently, a modified t-distributed stochastic neighbor embedding (M-tSNE) algorithm is developed to achieve the features reduction to further improve the diagnosis efficiency. Finally, the low-dimensional representations after features reduction are as the inputs of softmax classifier to recognize the fault conditions. The proposed method is applied to the fault diagnosis of REB. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for the actual engineering applications compared with other existing methods.

The importance of bearing stiffness and load when estimating the size of a defect in a rolling element bearing

The change in the static stiffness of a bearing assembly is an important discriminator when determining the size of a defect in a rolling element bearing. In this article, the force–displacement relationships for defective bearings under various static radial loadings at various cage angular positions are analytically estimated and experimentally measured and analyzed. The study shows that the applied load has a significant effect on the static stiffness variations in defective rolling element bearings. The experimental measurements of the effect of the defect size on the varying stiffness of the bearing assembly, which has not been shown previously, provides valuable knowledge for developing methods to distinguish between defective bearings with defects that are smaller or larger than one angular ball spacing. The methods and results presented here contribute to the wider experimental investigation of the effects of loadings on the varying static stiffness of defective bearings and its effects on the measured vibration signatures. A large data set was obtained and has been made publicly available.

Fault detection of rolling element bearings using optimal segmentation of vibrating signals

Abstract Change detection and diagnosis are important research directions and activities in the field of system engineering and conditional maintenance of equipments and industrial processes. The paper promotes a new method for change detection and optimal segmentation of vibrating data obtained during operation of rolling element bearings (REB). After a description of the bearing faults and dynamic simulation of REB, the paper makes a review of the change detection and segmentation approaches, that could be used in REB fault detection and diagnosis. A new approach for change detection and optimal segmentation of vibrating signals, aiming to determine the change points in signals generated by the faults, produced during REB operating, is presented; the efficiency of the segmentation method is proven using Monte Carlo simulations for different signal models, including models with changes in the mean, in FIR, and AR model parameters, frequently used in processing vibrating signals. In the final part, the paper analyses some experimental results obtained using this approach and data from the Case Western Reserve University Bearing Data Center.

Automatic band selection algorithm for envelope analysis

Envelope analysis has been widely used to detect early stage faults of rolling element bearings. The primary initial step of envelope analysis is the proper selection of the resonance band for demodulation. Current band selection methods, such as wide band selection, “power spectral density” comparison, and selecting the accelerometer resonance band have limitations such as disturbance of the wide band, the need for a healthy signal for comparison, and the implementation of specialized sensors. In this study, an enhanced method of resonance band selection for envelope analysis was developed. The developed method implements high-pass filtering and “time synchronous averaging” to remove dominant speed-dependent (nonsynchronous and synchronous) spectral contents of a vibration signal. Wavelet packet transform and “root mean square” were then applied to determine the energy distribution of the residual signal. The band with the highest energy (resonance band) was selected for envelope analysis. An experimental study was designed for cross-validation of the developed method. The developed method in this study is more practical than current band selection methods and has no special requirement for sensors. The developed algorithm can be implemented as a processing algorithm in a commercial vibration analyzer, which enhances its ability in early-stage bearing fault detection.

Rolling element bearings localized fault diagnosis using signal differencing and median filtration

With the increase complexity of bearings’ processing algorithms and the growing trend of using computationally demanding algorithms, it is advantageous to provide analysts with a simple to use and implement algorithm. In this spirit, this paper combines simple functions to provide machine condition analysts with the capacity to diagnose bearing faults without all the complexity and jargon that comes with existing methods. The paper proposes a simplified surveillance and diagnostic algorithm for diagnosing localized faults in rolling element bearings using measured raw vibration signals. The proposed algorithm is based on analyzing the frequency content obtained from applying a median filter on the squared derivative signal (first or higher derivatives) of the vibration signal. The combination of signal differencing and median filters provides a squared envelope signal, which can be used directly to diagnose faults. Signal differencing gives a measure of jerk forces and lifts the high frequency content of the signal. To select the optimum order of differentiation, Kurtosis and maximum correlated kurtosis (MCK) are proposed. Median filter usage represents a better alternative of normal low pass filtration. This completely suppresses impulses with large magnitudes, which may interfere with the diagnosis. The length of the median filter (odd number 3, 5, 7 etc.) is selected as such to include the first 10 harmonics of the defect frequency. Simulated signals are used to demonstrate the efficiency of the proposed algorithm and give insights into the choices of the differentiation and smoothening orders. The proposed processing algorithm gives a first measure (surveillance) for detecting localized faults in rolling element bearings in a very simple way and can be employed in online learning and diagnosis systems. Results obtained from applying the algorithm on complex vibration signals from two types of gearboxes are compared with a well-established semi-automated technique with good correspondence.

Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults

Rolling element bearings (REBs) play a most critical role in various industrial machinery. A clearly and in-depth understanding of vibration characteristics of REBs is very helpful for condition monitoring and diagnosis applications for the machinery. This work presented a comprehensive review of dynamic modelling and analysis methods for predicting the vibration characteristics of REBs with and without localized and distributed faults. Main capabilities and limitations of those methods for both the localized and distributed faults have been described. Explanations for the generations of the bearing vibrations were also reviewed. A summary of the literature was given followed by the recommendations of current and future research works. Moreover, recent challenges, directions and implications in research works on the dynamic modelling and analysis methods of the localized and distributed faults in REBs have also been conducted.

Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis.

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods.