Outer Race Fault Research Articles

Experimental and numerical study of an angular contact ball bearing vibration response with spall defect on the outer race

Angular contact ball bearings are widely used in rotary machines for their combined loads capacity, i.e., simultaneously acting radial and axial loads. Spall defect is one of the most important potential failure modes of the rolling element bearings. The main motivation of this study is to achieve a true perception of the spall defect influence on the angular contact ball bearing to predict bearing failure. In this paper, simulation and experimental analysis are performed for an angular contact ball bearing with a spall defect in the outer race. At first, the bearings without and with outer race defect are modeled, and after extracting the governing equations, they are solved using function ODE45 in MATLAB. This function implements a Runge–Kutta method with a variable time step for efficient computation. Then the vibration response in different conditions of rotating speed and axial preload is simulated. A bearing test bench is designed to perform the experimental tests. The defect is contrived in the outer race of a healthy bearing, and the vibration signals at both conditions (healthy and defective) are collected. The spall defect in the outer race is considered to have a cylindrical shape to close the model to real conditions as much as possible. The results are then presented in the form of time-domain signals and fast Fourier transformations (FFT) graphs. The FFT results showed that defect in the outer race produces dominant peaks with a suitable similarity to each other in both simulations and experimental tests.

Experimental comparison of acoustic emission sensors in the detection of outer race defect of angular contact ball bearings by artificial neural network

In this paper, three different types of acoustic emission sensors have been applied to detect the outer race defect of angular contact ball bearings. The sensors include R6a, which is a general-purpose, WSa, wideband sensor and Pico, miniature sensor. All three sensors differ in size and operational frequency range and sensitivity. In this research, using signal analysis methods, different features and parameters in time-domain have been extracted from the signal and different four states (healthy and three faulty states) have been identified based on them. An artificial neural network used for separating, detection and classification of defect sizes. In the first comparison of the signals, the results showed that R6a was better for absorbing acoustic emission energies and for data acquisition and processing than the other two sensors. But by creating the neural network and selecting different parameters, therefore all three sensors could be useful in diagnosing the faults.

Multi-scale deep intra-class transfer learning for bearing fault diagnosis

The tremendous success of deep learning in machine fault diagnosis is dependent on the hypothesis that training and test datasets are subordinated to the same distribution. This subordination is difficult to meet in practical scenarios of industrial applications. On the one hand, the working conditions of rotating machinery can change easily. On the other hand, vibration data and labels are difficult to obtain to train a specific model for each working condition. In this study, we solve these problems by constructing a novel deep transfer learning model called multi-scale deep intra-class adaptation network, which first uses the modified ResNet-50 to extract low-level features and then constructs a multiple scale feature learner to analyze these low-level features at multiple scales and obtain high-level features as input for the classifier. Pseudo labels are then computed to shorten the conditional distribution distance of vibration data collected under different working loads for intra-class adaptation. The proposed method is validated using two datasets to recognize the bearing normal state, the inner race, the ball and outer race faults, and their fault degrees under four different working loads. The high-precision diagnosis results of 24 transfer learning experiments reveal the reliability and generalizability of the constructed model.

Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

Adaptive parametric dictionary design of sparse representation based on fault impulse matching for rotating machinery weak fault detection

Fault detection and diagnosis techniques for rotating mechanical components are crucial for the safety, efficiency, reliability of mechanical systems. In recent decades, many sparse representation approaches based on parametric dictionary design have been successfully applied to extract fault features from vibration signals. However, most of them rely on the classical correlation filtering algorithm (CFA), which has some shortcomings, such as poor antinoise ability and an extensive computation load. To address these issues, this paper proposes a novel water cycle algorithm (WCA)-optimized fault impulse matching algorithm (FIMA) for parametric dictionary design, which can match the underlying fault impulse structure of fault signals by applying a comprehensive strategy of local matching and global matching. With the proposed method, the accuracy of wavelet parameter identification and calculation efficiency are improved significantly. In addition, the method is suitable for constructing parametric dictionaries with different wavelet bases according to different rotating machinery components. The effectiveness of the proposed method is verified by the simulated signals, as well as the practical wheelset bearing faults (outer race fault and inner race fault) and gearbox (broken tooth) signals. The results of a comparison study show that the proposed method outperforms the classical CFA, K-SVD and whale optimization algorithm-optimized orthogonal matching pursuit in weak fault feature extraction.

Chaotic dynamics of cylindrical roller bearing supported by unbalanced rotor due to localized defects

This article presents a nonlinear vibration signature study of high-speed defective cylindrical roller bearings under unbalance rotor conditions. Qualitative analysis is conducted considering a spall defect of a specific size on major elements such as outer race, inner race, and rollers. A spring-mass model with nonlinear stiffness and damping is formulated to study the dynamic behavior of the rotor-bearing model. The set of nonlinear differential equations are solved using the fourth-order Runge–Kutta method to predict the characteristics of the discrete spectra and analyze the stability of the system. The results show that higher impulsive forces are generated because of outer race defects than defects in the inner race and roller. This can be explained as every time the roller passes through the defect in the outer race during rotation, the energy is released. However, in the case of both the roller and inner race defects, the impulsive force generated in the load zone is averaged because of the force generated in the unloading zone. The route to chaos from periodic to quasiperiodic response has been observed and analyzed that vibration signature is very much sensitive not only to the defects of bearing components but also to the rotor speed.

Diagnosis of bearing faults using multi fusion signal processing techniques and mutual information

Bearing is a widely used rotating component in most of the industrial machinery. Failure of bearings can incur substantial losses in the industries. During operation, to prohibit failure in bearing, it becomes necessary to identify faults that occur in bearings. In the present work, bearing vibration signals have been taken for the detection of faults in bearings. In the next step, features obtained from various signal processing techniques such as ensemble empirical mode decomposition (EEMD), walsh hadamard transform (WHT) and discrete wavelet transform (DWT) have been used to detect bearing faults (inner race defect, outer race defect, and ball defects). To select the mother wavelet, the maximum energy to entropy ration criteria has been used. Mutual Information feature ranking algorithm is used to select the relevant features. Machine learning techniques such as Random Forest, Support Vector Machine, Artificial Neural Network, and IBK are used. Training and tenfold cross-validation procedures applied to all ranked features. Results reveal that random forest gives 100 % training accuracy with one ranked feature and 98.43 % ten-fold cross-validation accuracy with seven features. From the results, it is observed that the proposed methodology can be reliable and it may serve as an effective tool for fault diagnosis of bearing.

Identification of Bearing Faults using Wavelet Transform

This research is concerned with description of a scheme for bearing’s localized defect detection based on wavelet packet transform (WPT). WPT provides a high resolution time-frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element on the defect, are detected. The objective of this work is to emphasis on the outer race defect, inner race defect and ball defect. In modern industrial scenario, there is increasing demand for automatic condition monitoring that reduce the gap between digital model and actual product. With reliable condition monitoring, faults such as machine element failures could be identified in their early-stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories.

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

Many methods have been developed for machinery fault diagnosis addressing only Gaussian noise reduction, the major weaknesses of these methods are their performance for non-Gaussian noise suppression. Modulation signal bispectrum (MSB) is a useful signal processing method with significant advantages over traditional spectral analysis as it has the unique properties of Gaussian noise elimination and demodulation. However, it is susceptible to non-Gaussian noise that normally occurs in the practical applications. In view of the deficiency of MSB, in this paper, an autoregressive (AR) modeling filter was developed based on non-Gaussian noise suppression to improve the performance of MSB for machinery fault detection. The AR model was considered as a pre-filter process unit to reduce the non-Gaussian noise. And the order of the AR model, which can affect the performance of the AR filter, was determined adaptively using a kurtosis-based indicator. However, the existing nonlinear modulation components remain in the AR filtered signal. The MSB was then applied to decompose the modulated components and eliminate the Gaussian noise from the filtered signal using AR model for the fault feature extraction accurately. The advantage of the AR model can effectively manage non-Gaussian noise, whereas the MSB can suppress Gaussian noise and is illustrated in the simulation signal. Furthermore, the proposed AR-MSB method was applied to analyze the vibration signals of defective bearings with outer race and inner race faults. By comparison, the proposed approach had a superior performance over conventional MSB and fast kurtogram in extracting fault features and was precise and effective for rolling element bearing fault diagnosis.

Mean Shift Clustering-Based Analysis of Nonstationary Vibration Signals for Machinery Diagnostics

Vibration analysis is a powerful tool for condition monitoring of rotating machinery. In the nonstationary case, this analysis often involves denoising and extraction of the time-varying harmonic components buried within the vibration signal. However, the complexity of many contemporary techniques—especially in relation to nonstationary signals—and their dependence on prior knowledge of the system kinematics in order to be effective is an inhibitor to autonomous fault detection and monitoring of nonstationary systems. In this article, a nonparametric, blind spectral preprocessing approach to simultaneously denoise and extract the harmonic content from nonstationary vibration signals is presented. The proposed approach utilizes mean shift clustering in conjunction with the short-time Fourier transform to separate time-varying harmonics from background noise within the frequency spectrum, without the need for a priori knowledge of the system. The technique is fully invertible, allowing the time signals corresponding to the separated time-varying harmonic and residual components to be reconstructed. The performance of the proposed technique is compared against existing preprocessing methods and validated using several industrial data sets: first, using vibration data obtained from a low-speed, nonstationary industrial automated people mover gearbox, next, using vibration data from an aircraft engine containing outer race faults, and finally, using nonstationary vibration data from a wind turbine containing frequent speed fluctuations.

An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis.

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Fault diagnosis for rolling element bearing using variational mode decomposition and l1 trend filtering

In order to extract the faint fault features of bearings in strong noise background, a method based on variational mode decomposition and l1 trend filtering is proposed in this study. In the variational model, the mode number κ is determined difficulty, thus l1 trend filtering is applied to simplify the frequency spectrum of the original signals. In this case, this parameter can be defined easily. At the same time, a criterion based on kurtosis is used to adaptively select the regularization parameter of l1 trend filtering. The combined approach is evaluated by simulation analysis and the vibration signals of damaged bearings with a rolling element fault, an outer race fault and an inner race fault. The results demonstrate that the hybrid method is effective in detecting the three bearing faults. Moreover, compared with another approach based on multiscale morphology and empirical mode decomposition, the proposed method can extract more bearing fault features.

A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation.

Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals.

Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission

In this study, an experimental system was built to acquire vibration and acoustic emission (AE) signals from faulted bearings methodology based on cepstrum pre-whitening (CPW), tested for vibration signals, and was applied for both types of signals to compare and enhance results on machining condition monitoring. The methodology was applied to 9 vibration and 9 AE signals from the experimental system database. For the 18 analyzed signals, in 5 the identification of fault components was easily made, in 12 the fault identification was possible, and in 1 the identification was not completed. The comparison between vibration and AE from 9 tests of experimental system results in 6, vibration has a better result than AE, specifically in the inner race and rolling element faults, for the remaining 3 tests that correspond to outer race fault, AE has a better result.

Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system

This paper concerns the automatic diagnosis of ball bearing defects in industrial geared motor based on statistical indicators and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The approach consists of three essential steps: the first is the extraction of statistical indicators from the root mean square (RMS) of the raw vibration signals measured experimentally for different states of the bearing (healthy and in the presence of defects). The second step consists of the selection of the more relevant indicators, and finally the introduction of these indicators to the ANFIS network in order to classify the various defects in the bearing (inner and outer race faults, and combined fault). A test campaign was conducted on an industrial installation (Wheeled Conveyor) to collect data as the RMS trend of the raw vibrations using adequate instrumentation in order to verify the validity of the method in real test conditions. The obtained results show that the proposed approach can reliably detect and classify various faults at different speeds of rotation of the electric motor. The effectiveness of the proposed method was also approved by using additional test data.

A mechanical fault detection strategy based on the doubly iterative empirical mode decomposition

Empirical mode decomposition (EMD) has been widely used in the fault detection of rotating machineries. However, the deviation of the local extrema can decline the decomposition performance of the conventional EMD. This paper proposes a fault detection strategy based on the doubly iterative empirical mode decomposition (DIEMD), where the local extrema are fixed during the sifting process. This strategy combines the DIEMD to obtain decomposed intrinsic mode functions (IMFs), correlation analysis to select the representative IMF and envelope spectrum analysis to extract the fault feature frequency. The decomposition performance of the DIEMD was validated through the dual signal analysis. A numerical simulation and experimental investigations on four fault cases (the bearing with the inner race and outer race fault, the gear with broken teeth and the piston pump with cylinder block fault) of rotating machineries were carried out. The amplitude enhancement index (AEI) was defined as an indicator of the performance improvement. Results showed that the proposed strategy is applicable and effective for the fault detection of rotating machineries with AEI being 17.8%, 8.6%, 23.0%, 19.0% and 4.1%, respectively.

Smart Fault-Detection Machine for Ball-Bearing System with Chaotic Mapping Strategy.

In this paper, a set of smart fault-detection approach with chaotic mapping strategy is developed for an industrial ball-bearing system. There are four main statuses in this ball-bearing system: normal, inner race fault, outer race fault, and ball fault. However, it is hard to simply classify each of them through their vibration signals in time-series. By developing a nonlinear error dynamic system as well as a chaotic mapping strategy, the signals in the time series can be converted into the chaotic domain, which are revealed in 3D phase portraits. Further, through collocation of clustering methods, such as Euclidean distance (ED) and the kernel method of K-means (KM), the proposed 3D phase portraits of each different state can be efficiently identified through checking the autonomously adjusted ranges of feature values. The experiment results show that the proposed smart detection approach is effective and feasible, and the accuracy of detection in the testing stage is close to 100%.