Life Prediction Of Bearings Research Articles

Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach

Bearing Life Prediction With Informed Hyperprior Distribution: A Bayesian Hierarchical and Machine Learning Approach

The Transfer Prediction Method of Bearing Remain Use Life Based on Dynamic Benchmark

For data-driven remaining useful life (RUL) prediction of rolling bearing (RB), deep learning methods usually do not consider the difference in distribution due to different operating conditions, which adversely affects the prediction results. Recently, transfer learning has been a research hotspot, and it can mitigate the above problem effectively. However, for different bearings under the same working condition, the data distribution changes due to the location and time of failure. To solve these problems, a transfer prediction model based on the dynamic benchmark (DB) has been proposed to predict the RUL of bearings in this study. The method is divided into three processes. First, a fully convolutional variational autoencoder is used to perform unsupervised learning; next, a domain adaptation method based on the DB is employed for reconstruction; finally, extracted hidden layer features are input to the fully connected layer of the proposed model to obtain the final prediction result. The prediction and health management (PHM) Challenging 2012 and XJTU-SY RB accelerated life test datasets were used for verification. The results showed that the proposed method could significantly improve prediction accuracy and had good stability and generalizability.

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories.

Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision-making for predictive maintenance (PdM) of rotating machinery. However, the individual heterogeneity and different working conditions of bearings make the degradation trajectories of bearings different, resulting in the mismatch between the RUL prediction model established by the full-life training bearing and the testing bearings. To address this challenge, this paper proposes a novel RUL prediction method for roller bearings that considers the difference and similarity of degradation trajectories. In this method, a feature extraction method based on continuous wavelet transform (CWT) and convolutional autoencoder (CAE) is proposed to extract the deep features associated with bearing performance degradation before the degradation indicator (DI) is obtained by applying the self-organizing maps (SOM) method. Next, a dynamic time warping (DTW) based method is applied to perform the similarity matching of degradation trajectories of the training and testing bearings. Driven by the historical DIs of the given bearing, the grey forecasting model with full-order time power terms (FOTP-GM) is applied to model the degradation trajectory using a parameter optimization method. Then, the failure threshold of the given testing bearing can be determined using a data-driven method without manual intervention. Finally, the RUL of the given testing bearing can be estimated using the preset failure threshold and the optimized degradation trajectory model of the given testing bearing. The experimental results show that the proposed method retains the individual differences of bearing degradation trend, realizes the independent and reasonable bearing failure threshold setting, and improves the prediction accuracy of RUL.

Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings

A variety of data-driven methods have been proposed to predict remaining useful life (RUL) of key component for rolling bearings. The accuracy of data-driven RUL prediction model largely depends on the extraction method of performance degradation features. The individual heterogeneity and working condition difference of rolling bearings lead to the different performance degradation curves of rolling bearings, which result in the mismatch between the established RUL prediction model by the training rolling bearings and the test rolling bearings. If a feature is found, which can reflect the consistency of the performance degradation curve of each rolling bearings, and give the indicator to determine the node and predictable interval of the declining period, the accuracy of the RUL prediction model will be improved. To solve this problem, a new feature extraction method based on the data-driven method, namely, Fitting Curve Derivative Method of Maximum Power Spectrum Density (FDMPD), is proposed to extract the performance degradation features of the same or similar rolling bearings from the historical state monitoring data in this article. The FDMPD can make the performance degradation feature curves of life cycle, which takes on consistency trend for different rolling bearings, and the starting point of the rolling bearings to enter the degenerating period is defined and the working stage of rolling bearings is divided. Based on this, the kernel extreme learning machine (KELM) and weight application to failure times (WAFT) are combined with FDMPD to establish a new RUL prediction model of rolling bearings, which can effectively realize the RUL prediction of rolling bearings. The whole life cycle data of rolling bearings are used to verify the validity of the RUL prediction model. The experimental results show that the established RUL prediction model can accurately predict the RUL of rolling bearings. It has the advantages of rapidity, stability, and applicability.

Remaining useful life prediction of planet bearings based on conditional deep recurrent generative adversarial network and action discovery

Planet bearing vibration signals are complex and non-stationary owing to the time-varying vibration transmission path from fault point to accelerometers. In addition, due to the influence of speed and load variation, planetary gearboxes often operate under varying conditions, which causes the acquired vibration signals to be nonlinear. These seriously degrade the performance of the RUL prediction methods. Moreover, there are only few training samples for the RUL prediction of planet bearings. To address these problems, an RUL prediction method of planet bearings using conditional deep recurrent generative adversarial network (C-DRGAN) and action discovery (AD) is presented. First, gated recurrent unit neural network is integrated with conditional generative adversarial network to construct the C-DRGAN, which extracts fault features from nonlinear and non-stationary signals so as to realize the RUL prediction of planet bearings under small samples and varying conditions. Then, the training algorithm based on AD is employed to train the C-DRGAN to enhance convergence speed and reduce training time. Finally, a multiple linear regression classifier is utilized to predict the RUL of planet bearings. The performance of the presented method is validated through an accelerated fatigue life experiment of planet bearings. Experimental analyses demonstrate that this method possesses strong processing adaptability to nonlinear and non-stationary signals and obtains excellent performance under small samples.

Remaining Useful Life Prediction for Bearings Based on a Gated Recurrent Unit

Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predict the RUL of bearings. It uses feature extraction, an attention mechanism, and uncertainty analysis. First, the features embedded in the bearings’ vibration signals are extracted. Second, a stacked gated recurrent unit (GRU) is constructed to predict the bearing RUL. A novel attention mechanism based on dynamic time warping (DTW) is developed to improve the performance of information extraction, and a Bayesian approach is employed to analyze the prediction uncertainty. Finally, the proposed approach is validated using two benchmark-bearing data sets. The results show that the proposed approach can predict the bearing RUL effectively, and the prediction uncertainty can also be evaluated.

Research on Bearing Life Prediction Method Based on EMD and Gray Model

In order to predict the residual life of bearing based on practical signals, the Empirical Mode Decomposition (EMD) is proposed and it is used to decompose the signal and filter out the invalid frequency components in the signal. The root means square (RMS) values of the pre-processed life cycle data samples were calculated. It is used to describe the health status of bearings and form a list degradation characteristic quantity. Then the list is used to train the Grey Model. The characteristic quantity trend is estimated by the trained Grey Model. It is verified by testbed and engineering practice. The results show that this method can predict bearing life effectively and with high precision.

A Bearing Life Prediction Method of Improving Smooth Degree and the Background Value

Bearings, as a component in many complex weapons, can be used to reduce friction to improve the efficiency of equipment. Bearing CV value can quantify the working performance of bearings, which can act as a reference standard for staff to evaluate the working condition of bearings. According to the known data, the real CV value of the bearing is calculated in this paper. In order to improve the smoothing ratio, the data is processed by the idea of data transformation and the background value is optimized by the new formula. The two improve the GM (1,1) model and simulate the predicted bearing CV and calculate the moment of failure by this model, which is compared with the traditional GM (1,1) and the improved GM (1,1) by cumulative method in terms of error and accuracy. It is verified that the average relative error and the model prediction accuracy of the model prediction life are 0.0185 and 98.15% respectively after the improvement of the stability and background value. Therefore, this method has certain practical value in engineering, and is more effective than the cumulative GM (1,1) model.

Correlation Analysis and Augmentation of Samples for a Bidirectional Gate Recurrent Unit Network for the Remaining Useful Life Prediction of Bearings

The remaining useful life (RUL) prediction of bearings plays a crucial role in ensuring the safe operation of machinery and reducing costly maintenance. Sufficient degradation information contributes to the accuracy of RUL prediction, but the effective retention of degradation information is still a difficult task, especially for small samples. Thus, a novel RUL prediction method was proposed using data augmentation (DA) and a deep bidirectional gate recurrent unit (DBGRU). Initially, a feature selection method without consideration of the operating time that exploited the mutual information of different units, namely, the main factor correlation index (MFCI), was proposed to construct suitable feature input. In addition, a higher volume of training samples was generated with an effective DA technique, Mixup, and a bidirectional gate recurrent unit (BGRU) layer was utilized to further explore the subtle degradation information with the mixed data and the raw data, which also improved the robustness and generalization of the model. Ultimately, the advantage of the proposed method was demonstrated by a comparison with several state-of-the-art prediction models for the same circumstance. The test results showed that the proposed method was promising and that it had good prediction accuracy and robustness for different working conditions.

Remaining useful life prediction of rolling bearings based on the three-parameter Weibull distribution proportional hazards model

In order to accurately predict the remaining useful life (RUL) of rolling bearings, a novel method based on the threeparameter Weibull distribution proportional hazards model (WPHM) is proposed in this paper. In this new method, degradation features of the bearing vibration signals were calculated in the time, frequency and time-frequency domains and treated as the input covariates of the predictive WPHM. Essential knowledge of the bearing degradation dynamics was learnt from the input features to build an effective three-parameter WPHM for bearing RUL prediction. Experimental data acquired from the run-to-failure bearing tests of the intelligent maintenance system (IMS) was used to evaluate the proposed method. The analysis results demonstrate that the proposed model is able to produce accurate RUL prediction for the tested bearings and outperforms the popular two-parameter WPHM.

Rolling bearing remaining useful life prediction via weight tracking relevance vector machine

The application scenarios of rotating machinery are becoming increasingly complicated due to the rapid development of the manufacturing industry. The remaining useful life (RUL) prediction of rolling bearings has gradually been considered in many industry fields for ensuring the safety and reliability of whole systems. As an effective way to analyze data, the relevance vector machine (RVM) approach holds great potential for RUL prediction. However, the redundant features of rolling bearing vibration signals can easily lead to overfitting and low accuracy of the RVM model for RUL prediction. To conquer these issues, inspired by the idea of the boosting algorithm and ensemble learning, this paper proposes a new RVM model, called the weight-tracking relevance vector machine (WTRVM). Within the proposed WTRVM model, an adaptive sequential optimal feature selection method is designed to avoid overfitting by selecting the best features. The error between the prediction value of the RVM model and the true value is counted for the RVM model training and weight tracking. The most accurate model can be obtained when all selected features have been trained. Finally, the proposed WTRVM algorithm is experimentally demonstrated to be effective for the RUL prediction of rolling bearings.

Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features

Abstract Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well understood yet. To complement model-based prognostics, data-driven methods have been increasingly used to predict the remaining useful life (RUL) of bearings. As opposed to other machine learning methods, ensemble learning methods can achieve higher prediction accuracy by combining multiple learning algorithms of different types. The rationale behind ensemble learning is that higher performance can be achieved by combining base learners that overestimate and underestimate the RUL of bearings. However, building an effective ensemble remains a challenge. To address this issue, the impact of diversity in base learners and extracted features in different degradation stages on the performance of ensemble learning is investigated. The degradation process of bearings is classified into three stages, including normal wear, smooth wear, and severe wear, based on the root-mean-square (RMS) of vibration signals. To evaluate the impact of diversity on prediction performance, vibration data collected from rolling element bearings was used to train predictive models. Experimental results have shown that the performance of the proposed ensemble learning method is significantly improved by selecting diverse features and base learners in different degradation stages.

A Probabilistic Framework for Remaining Useful Life Prediction of Bearings

This article presents a probabilistic framework for remaining useful life (RUL) prediction of bearings. This framework comprises two phases: 1) early fault detection, i.e., modeling the vibration signal as a series of graphs and then conducting graph spectrum analysis to detect the topological change of graph for early fault detection and 2) RUL prediction, i.e., adopting a probabilistic model for bearing RUL prediction given the detected early fault. Specifically, the least square method and noninformation distribution are employed to set the prior knowledge within this model. Meanwhile, a state-of-the-art Markov chain Monte Carlo method, No-U-Turn sampler, is investigated in posterior sampling for predicting RUL and outputting uncertainty. As a result, this framework can work without the training data from other bearings. Besides, it is an unsupervised framework that makes operators free from manual parameters setting and costly tuning runs. In addition to theoretical derivation, the proposed framework is validated using both synthetic data and real-world data and compared with the representative methods in this field. Excellent results show the effectiveness of this probabilistic framework in the RUL prediction of bearings.

Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

Rolling bearings play a pivotal role in rotating machinery. The remaining useful life prediction and fault diagnosis of bearings are crucial to condition-based maintenance. However, traditional data-driven methods usually require manual extraction of features, which needs rich signal processing theory as support and is difficult to control the efficiency. In this study, a bearing remaining life prediction and fault diagnosis method based on short-time Fourier transform (STFT) and convolutional neural network (CNN) has been proposed. First, the STFT was adopted to construct time-frequency maps of the unprocessed original vibration signals that can ensure the true and effective recovery of the fault characteristics in vibration signals. Then, the training time-frequency maps were used as an input of the CNN to train the network model. Finally, the time-frequency maps of testing signals were inputted into the network model to complete the life prediction or fault identification of rolling bearings. The rolling bearing life-cycle datasets from the Intelligent Management System were applied to verify the proposed life prediction method, showing that its accuracy reaches 99.45%, and the prediction effect is good. Multiple sets of validation experiments were conducted to verify the proposed fault diagnosis method with the open datasets from Case Western Reserve University. Results show that the proposed method can effectively identify the fault classification and the accuracy can reach 95.83%. The comparison with the fault diagnosis classification effects of backpropagation (BP) neural network, particle swarm optimization-BP, and genetic algorithm-BP further proves its superiority. The proposed method in this paper is proved to have strong ability of adaptive feature extraction, life prediction, and fault identification.

Reliability analysis of rolling bearings considering internal clearance

The internal clearance is a decisive parameter for life prediction and reliability analysis of rolling bearings. In this paper, a comprehensive reliability analysis model of rolling bearings is proposed by considering the internal clearance of rolling bearings. The model of bearings’ working clearance was established after investigating the effect mechanisms of effective interference, temperature, and centrifugal force on the clearance. The validation of the results is carried out by the reliability analysis of a real rolling bearing through the modeling of working clearance probability distribution and the research of life factor. The results indicate that the proposed methodology provides an additional reference way for the reliability analysis of rolling bearings.

Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing

Remaining useful life (RUL) prediction, allowing for mechanical predictive maintenance, reduces unplanned and expensive maintenance greatly. One of the great challenges of data-driven RUL prediction is to extract the features that describe the actual degradation process. This paper presents a health indicator (HI) construction method based on a sparse auto-encoder with regularization (SAEwR) model for rolling bearings. This paper includes two modules, HI construction and RUL prediction. In the stage of the HI construction, the original features are compressed and extracted by the SAEwR model. The extracted features are sorted according to the trendability, and the features with large trendability are selected to construct the HI by using minimum quantization error. In the module of RUL prediction, the maximum likelihood estimation method is used to estimate the parameters of the prediction model, and a particle filter-based RUL prediction with degradation model is proposed. The proposed method is benchmarked with variational auto-encoder, auto-encoder methods and principal component analysis. The data from PRONOSTIA and ABLT-1A platform support the value of our approach.

Failure Life Prediction of Hub Bearing in Composite Tooling

Composite work tools have many components, and complex shapes when compared to standard tools such as rotaries and plows. In addition, the component durability for the tools is critical because the load varies severely according to the soil conditions. This study predicts the fatigue life of the hub bearing in composite work tooling. The loads acting on the tools were measured based on field tests, and the loads acting on the hub bearing were derived using the load reconstruction method. The static safety factor and fatigue life of the hub bearing, loads, and contact stress acting on the inner and outer raceways and presence of truncation were analytically predicted based on the derived loads. The fatigue life of the bearing changed depending on the preload amount of the hub bearing. The bearing life was more than 3000 h for preloads of less than 40 μm, which satisfied the target life of 1200 h. The load acting on the inner and outer raceways of the bearing decreased and then increased as the preload amount of the bearing rose. The bearing contact area, maximum contact stress, and number of balls increased as the load applied to the hub bearing rose. The fatigue life, load, contact stress, and static safety factor of the hub bearing met all requirements, and no truncation occurred on the inner and outer raceways of the bearing. The test verified the achievement of the target life of 1200 h and confirmed that there was no breakage, cracking, or deformation of the bearing.

5-DOF Dynamic Modeling of Rolling Bearing with Local Defect considering Comprehensive Stiffness under Isothermal Elastohydrodynamic Lubrication

The sliding of the rolling element in the load zone would cause the bearing’s wear and failure at high speed under elastohydrodynamic lubrication (EHL) condition. Aiming at this phenomenon, considering lubrication oil film, time-varying displacement, radial clearance, and comprehensive stiffness, a five degree-of-freedom (DOF) dynamic model of rolling bearing with local defect is proposed based on isothermal EHL and which is validated by experimental data. The variation of oil film stiffness, comprehensive stiffness, and vibration characteristics of rolling bearing is studied under different speeds and loads. The results show that the lubricating oils with different viscosities have a certain influence on the bearing oil film thickness and comprehensive stiffness. As the load increases, the oil film stiffness and comprehensive stiffness would increase, and the oil film thickness would decrease. And as the tangential speed increases, the oil film stiffness would increase, and the oil film thickness and comprehensive stiffness would decrease. The vibration amplitude of the rolling bearing is enhanced with the increase of the rotation speed and the radial load. This model is helpful for the optimization, the correct use of lubricants, and life prediction of rolling bearing.

Remaining useful life prediction of rolling element bearings based on simulated performance degradation dictionary

Massive training samples are usually difficult to obtain in practice for remaining useful life (RUL) prediction of rolling element bearings (REBs). Building simulated data sets is an alternate solution. Some effective dynamic models have been established for instantaneous vibration behaviors of REBs. However, few researches have been done on the modeling of their long-term degradation processes. Thus, plenty of degradation data are obtained by dynamic modeling in this paper, and a novel performance degradation dictionary (PDD) is constructed for RUL prediction based on similarity. Firstly, the degradation process is divided into steady stage, defect initiation, defect propagation and damage stage. Considering the coupling excitation of time-varying morphology and stiffness, a comprehensive dynamic model is established to simulate the fault propagation. Secondly, a PDD which serves as the reference sets for RUL prediction is constructed by solving the response of the model. Hence, the similarity method can be enhanced to estimate the uncertainty of RUL. Finally, the effectiveness of the proposed method is verified by experimental degradation data under different working conditions and fault types.

Gated Dual Attention Unit Neural Networks for Remaining Useful Life Prediction of Rolling Bearings

In the mechatronic system, rolling bearing is a frequently used mechanical part, and its failure may result in serious accident and major economic loss. Therefore, the remaining useful life (RUL) prediction of rolling bearing is greatly indispensable. To accurately predict the RUL of the rolling bearing, a new kind of gated recurrent unit neural network with dual attention gates, namely, gated dual attention unit (GDAU), is proposed. With the acquired life-cycle vibration data of a rolling bearing, a series of root mean squares at different time instants are calculated as the health indicator (HI) vector. Next, the to-be HI sequence is predicted by GDAU according to the existing HI vector, and then the RUL of the rolling bearing is estimated. The experimental results show that the proposed GDAU can effectively predict the RULs of rolling bearings, and it has higher prediction accuracy and convergence speed than the conventional prediction methods.