Showing papers on "Bearing (mechanical) published in 2020"

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Abstract: Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

Performance Prediction Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy and Extreme Learning Machine

Abstract: Performance prediction is significant to monitor the health status of rolling bearings, which can greatly reduce the loss caused by potential faults in the whole life cycle of rolling bearings. It is a very important part of Prognostic and Health Management (PHM). In this article, a new performance degradation prediction (HMEPEM) method based on high-order differential mathematical morphology gradient spectrum entropy (HOMMSE), phase space reconstruction, and extreme learning machine (ELM) is proposed to predict the performance degradation trend of rolling bearings. In the proposed HMEPEM method, the HOMMSE method is used to extract the initial features of performance degradation from the raw bearing vibration signals and divide working stages. Then the phase space reconstruction is used to further extract more useful features from the initial features of performance degradation in order to construct a feature matrix, which is input into the ELM in order to build the performance degradation prediction model for predicting the performance degradation trend of rolling bearings. The proposed HMEPEM method is validated on the performance degradation data of rolling bearings provided by the PRONOSTIA platform. The results show that the proposed HMEPEM method can efficiently track the evolution of degradation and predict the performance degradation trend of rolling bearings.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Abstract: Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.

Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Abstract: Rolling bearings are the key components of rotating machinery. Thus, the prediction of remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper proposes a new method for RUL prediction of bearings based on time-varying Kalman filter, which can automatically match different degradation stages of bearings and effectively realize the prediction of RUL. The evolution of monitoring data in normal and slow degradation stages is a linear trend, and the evolution in accelerated degradation stage is nonlinear. Therefore, Kalman filter models based on linear and quadratic functions are established. Meanwhile, a sliding window relative error is constructed to adaptively judge the bearing degradation stages. It can automatically switch filter models to process monitoring data at different stages. Then, the RUL can be predicted effectively. Two groups of bearing run-to-failure data sets are utilized to demonstrate the feasibility and validity of the proposed method.

A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network.

Abstract: Normal operation of bearing is the key to ensure the reliability and security of rotary machinery, so that bearing fault diagnosis is quite significant. However, the large amount of data collected by modern data acquisition system and time-varying working conditions make it hard to diagnose the fault using traditional methods To break the predicaments, we propose a new intelligent fault diagnosis framework inspired by the infinitesimal method. The proposed model including three parts can ignore the effect of different rotational speeds. Firstly, the sample is segmented and every segment dimension is extended by input network to ensure the adequate information memory space. Secondly, the classification information is stored and transferred in the long short-term memory (LSTM) network and output to the third part. In this process, the working condition information is ignored because of the gate units function. Finally, the likelihood is given by output network to classify the health conditions. Besides, we propose a loss function combining all the output of every time step and employ dropout to train the model, which increase the training efficiency and diagnosis ability. The bearing datasets under time-varying speeds and loads are used to verify the proposed method. The application result shows that our method has higher accuracy with simpler structure, and is superior to the traditional method in bearing fault diagnosis. Moreover, we give a physical interpretation of the proposed model.

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers.

Abstract: Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model.

Abstract: Rolling element bearing is one of the critical components in rotating machines, and its running state determines machinery Remaining Useful Life (RUL). Estimating impending failure and predicting RUL of bearing is beneficial to schedule maintenance strategy and avoid abrupt shutdowns. This paper presents a novel method of RUL prediction of bearings, which can evaluate the degradation stage of bearings through dimensionless measurements and exploit the optimal RUL prediction through hybrid degradation tracing model in degradation stage. Two new measurements reflect the vibration intensity of bearings regarding normal vibration value. They can eliminate individual differences of bearings, improve sensitivity to the incipient defect of bearings, and reduce fluctuation. Moreover, they are helpful to detect the time to start prediction and set dimensionless failure threshold. SVM classifier is used to assess the degradation stage of bearing, which shows a high classification accuracy because of its excellent generalization ability and mathematical foundation. As input, the fitted measurements based on the generalized degradation model are used to train the SVM classifier. As output, five degradation stages are defined. However, actual measurements are used as inputs in the prediction process. According to the classification results, a hybrid degradation tracing model is utilized to exploit the optimal RUL prediction by tracking the degradation process of bearings. The proposed method is validated on the public IMS and PRONOSTIA bearing datasets, and its performance is compared with other methods on PRONOSTIA bearing datasets. The results show that the proposed approach is an effective way for RUL prediction of bearings within the prescribed error range. Given that the proposed measurements are dimensionless, this method can be applied under different operating conditions.

Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization

Abstract: Accurate fault information is critical for optimal scheduling of production activities, improving system reliability, and reducing operation and maintenance costs. In recent years, many fault diagnosis methods for rolling element bearings have been developed based on deep learning. Most of them are totally data-driven and do not consider the domain knowledge that has been used in fault diagnosis for years. Meanwhile, operating conditions such as rotating speed and load that have great influence on vibration signals are also ignored. It may cause a decrease in accuracy when the bearing type or operating condition changes. To address these problems, this article proposes a rolling element bearing fault diagnosis and localization approach based on multitask convolutional neural network (CNN) with information fusion. In the proposed approach, domain knowledge, operating conditions, and vibration signals are fused into a three-dimensional input that can be processed well by CNN. Then, a multitask CNN with dynamic training rates is constructed to simultaneously accomplish two tasks, fault diagnosis and localization. Experimental results on two rolling element bearing test beds with different bearing types and operating conditions are presented and compared with existing state-of-the-art approaches to demonstrate the effectiveness and accuracy of the proposed approach.

Support vector machines based non-contact fault diagnosis system for bearings

Abstract: Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines.

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Abstract: Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.

Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework

Abstract: Rolling bearing defects in induction motors are usually diagnosed using vibration signal analysis. For accurate detection of rolling bearing defects, appropriate feature extraction from vibration signals is necessary, failure of which may lead to incorrect interpretation. Considering the above fact, this article presents an autocorrelation aided feature extraction method for diagnosis of rolling bearing defects. To this end, the vibration signals of healthy as well as different faulty bearings were recorded using accelerometers and autocorrelation of the respective vibration signals were done to examine their self-similarity in time scale. Following this, several statistical, hjorth as well as non-linear features were extracted from the respective vibration correlograms and were subjected to feature reduction using recursive feature elimination technique. The dimensionally reduced top ranked feature vectors were subsequently fed to a random forest classifier for classification of vibration signals. A large number of experiments were carried out for (i) three different fault diameters at (ii) four different shaft speeds and also at (iii) two different sampling frequencies. Besides, for each condition, six binary class and one multiclass classification problem is also addressed in this paper, resulting in a total 112 different classification tasks. It was observed that the proposed method has yielded very high accuracy in identifying different bearing defects which can be practically implemented for automated bearing fault detection of induction motors.

A novel feature extraction method for bearing fault classification with one dimensional ternary patterns.

Abstract: Bearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (k-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type — inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined.

Fractional-order modeling and nonlinear dynamic analyses of the rotor-bearing-seal system

Abstract: Unexpected vibrations induced by sealing and bearing faults in the rotor-bearing-seal system seriously affect the health and reliability of the rotating machinery. Here, to study the vibration performances more accurately, the sealing force model is extended from a very narrow integer-order scope to a flexible fractional-order scope, and a novel fractional-order mathematical model of the rotor-bearing-seal system is established from the view of engineering applications by using the finite element method. As a pioneering work, the effect of the fractional order of sealing on the journal and rotor are analyzed under different rotational speeds. Besides, the dynamic characteristics of the rotor-bearing-seal system with the changing rotational speed, mass eccentricity of rotor, sealing clearance and sealing pressure drop at a specific fractional order of sealing are also studied in detail. Then some stability discussions of the system are presented, which is synchronous with some special frequency characteristics. Finally, the methods and results can efficiently provide a theoretical reference for the design and operation of the rotor-bearing-seal system and be applied to forecasting and diagnosing vibration faults of them.

Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis

Abstract: Bearing fault diagnosis is critical for rotating machinery condition monitoring since it is a key component of rotating machines. One of the challenges for bearing fault diagnosis is to accurately realize fault feature extraction from original vibration signals. To tackle this problem, the novel group sparsity signal decomposition method is proposed in this article. For the sparsity within and across groups’ property of the bearing vibration signals, the nonconvex group separable penalty is introduced to construct the objective function, leading to that the noise between the adjacent impulses can be eliminated and the impulses can be effectively extracted. Furthermore, since the penalty function is nonconvex, the convexity condition of the corresponding objective function to the global minimum is discussed. In addition, to improve the efficiency of parameter selection, this article presents an adaptive regularization parameter selection strategy. Simulation and experimental studies show that compared with the traditional method, the proposed method can better preserve the target components and reducing uncorrelated interference components for bearing fault diagnosis.

Electrical bearing failures in electric vehicles

Abstract: In modern electric equipment, especially electric vehicles, inverter control systems can lead to complex shaft voltages and bearing currents. Within an electric motor, many parts have electrical failure problems, and among which bearings are the most sensitive and vulnerable components. In recent years, electrical failures in bearing have been frequently reported in electric vehicles, and the electrical failure of bearings has become a key issue that restricts the lifetime of all-electric motor-based power systems in a broader sense. The purpose of this review is to provide a comprehensive overview of the bearing premature failure in the mechanical systems exposed in an electrical environment represented by electric vehicles. The electrical environments in which bearing works including the different components and the origins of the shaft voltages and bearing currents, as well as the typical modes of electrical bearing failure including various topographical damages and lubrication failures, have been discussed. The fundamental influence mechanisms of voltage/current on the friction/lubrication properties have been summarized and analyzed, and corresponding countermeasures have been proposed. Finally, a brief introduction to the key technical flaws in the current researches will be made and the future outlook of frontier directions will be discussed.

An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Abstract: Bearings are vital automation machine elements that are used quite frequently for power transmission and shaft bearing in rotating machines. The healthy operation of the bearings directly affects the performance of the rotating machines. Bearing faults may cause more vibration than normal in rotating machines, which wastes power. However, further bearing failures can cause vital damage to rotating machines. In this study, bearing vibration values are obtained through a special test setup. Different types and different sizes of artificial faults have been created in the bearings for the testing process. Data on these bearings are collected at different speeds. The purpose of the study is to diagnose faults in the bearings. In this context, a new approach is proposed. First, the one-dimensional local binary pattern (1D-LBP) method is applied to vibration signals, and all signal data are carried to the 1D-LBP plane. Statistical features are obtained from the signals in the 1D-LBP plane by using these features, and then the vibrational signals are classified by the gray relational analysis (GRA) model. Four different data sets are organized to test the proposed approach. The results of the test process with this proposed model have an accuracy of 99.044% for Dataset1 (different speed −300 rpm intervals), 94.224% for Dataset2 (different speed −60 rpm intervals), and 99.584% for Dataset3 (fault size (mm)); a 100% average success rate is observed for Dataset4 (fault type - error free bearing (EFB), inner ring fault (IRF), outer ring fault (ORF), and ball fault (BF)).