Bearing Remaining Useful Life Prediction Research Articles

RUL Prediction of Rolling Bearings Based on a DCAE and CNN

Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions

As a key component in the machinery, the health of bearings directly affects working performance of machinery. Recently, many data-driven methods have been proposed to predict remaining useful life (RUL) of rolling bearings. However, most methods neglected the problem of data distribution difference caused by different operation conditions, which will lead to prediction performance deteriorating greatly on other bearings. To solve the domain shift problem in bearing RUL prediction, a sparse domain adaption network (SDAN) is proposed in this study. Firstly, an adaptive selection mechanism is proposed to select important input features in SDAN. Besides, a novel feature extractor, adaptively convolutional neural network (ACNN) is proposed to capture essential information from the selected features by adjusting receptive fields adaptively. The sparse feature selection layer is developed to suppress noise and remove ineffective features based on the noise filtering of sparse representation. Besides, the sparse domain adaption is used in SDAN by integrating domain-adversarial leaning and unsupervised sparse domain alignment to solve the problem of data distribution shift. Finally, the effectiveness of SDAN is verified on the PRONOSTIA rolling bearing dataset. The results demonstrate that SDAN can extract essential features and provide transferable RUL prediction performance under different working conditions.

MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings

Rolling bearings are important components of industrial rotating machinery and equipment. The prediction of the remaining useful life (RUL) of rolling bearings is of great significance for improving the safety of the machine, reducing the economic and property losses caused by the failure of the bearings. However, for the task of predicting the RUL of rolling bearings, the information of the past time and the future time are as important as the information of the current time. In order to make better use of the extracted features for RUL prediction of rolling bearings, this paper has proposed a novel deep learning framework of multi-scale long-term recurrent convolutional network with wide first layer kernels and residual shrinkage building unit (MSWR-LRCN). The major difference from the previous deep neural network is that our new network organically combines the attention mechanism with multi-scale feature fusion strategy, and improves the anti-noise ability of the entire network. In addition, moving average (MA) method and a polynomial fitting model are also used, which help predict the RUL of rolling bearings effectively. The results show that this method has improved the prediction accuracy compared with the existing methods.

An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability

The prediction of remaining useful life (RUL) of rolling bearing is core content of equipment prognosis and health management. For rolling bearings, the degradation trend can be divided into multiple stages, and each stage has uncertain changes. Therefore, a new approach of bearing RUL prediction based on stochastic process model is proposed in this paper. Firstly, a new stochastic degradation model is established, which integrates the characteristics of multistage and multi-variability of degradation trend. Then, the statistical process control (SPC) is applied to stage division for the first time, which divides degradation stages and adaptively switches degradation models. At the same time, in the absence of prior information, update model parameters online by using parameters estimation method based on expectation maximization (EM) algorithm and predict RUL distribution in different degradation stages. Finally, the effectiveness of this approach is verified by empirical study of simulation example and XJTU-SY bearing data. The results show that this approach can divide different stages of rolling bearing and provide RUL prediction of corresponding stages.

Memory-enhanced hybrid deep learning networks for remaining useful life prognostics of mechanical equipment

Remaining useful life (RUL) prognostics is one of the most important parts in prognostics and health management, which can effectively avoid sudden accidents and economic losses caused by mechanical equipment failure. Previous artificial intelligence based prognostics method depends on manual feature extraction, which requires a lot of expert experience and prior knowledge for feature design. This paper presents a memory-enhanced hybrid deep learning network (MEHDLN) combining convolution neural network and recurrent neural network, which contains convolution layer, pooling layer, bidirectional long short-term memory (BLSTM) layer and fully connected (FC) layer. It can not only extract local robust features from raw signals but also capture time-dependency in sequence sensor signals. The proposed MEHDLN model is verified by two experimental cases including rolling element bearing and turbine engine RUL prediction. Experimental results demonstrate the superiority of the MEHDLN over other state-of-the-arts.

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.

A Remaining Useful Life Prediction Approach Based on Low-Frequency Current Data for Bearings in Spacecraft

Remaining useful life (RUL) prediction of rolling bearings brings benefits for maintenance of spacecrafts. Vibration signals are widely used for RUL prediction. However, under some situations such as high-speed rotation of bearings, vibration signals are quite easily disturbed by noise and might be tough to collect due to inappropriate installation of accelerometers. Therefore, in this paper, stator current signals are considered as health indicator for bearing RUL prediction. Based on stator current signals, feature extraction and trajectory tracking of signals suffer two challenges: 1) degradation tracking of downlinked stator current data is restricted due to low-frequency downlink from spacecrafts to stations; 2) the accuracy of health state estimation is constrained by artificial feature selection and prior knowledge about bearings. To overcome these issues, a novel RUL prediction approach is proposed in this work. An adaptive feature selection strategy based on autoregression model and backpropagation neural network is applied. Finally, an improved RUL prediction framework is introduced under down-sampled signals, which combines compressed sensing and deep learning model. A case from Paderborn university and a case from life test of bearing on control moment gyro were investigated. Experimental results support the validity of proposed approach in terms of lower prediction error than related works.

Deep Learning-based Health Indicator for Better Bearing RUL Prediction

The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoencoder-based health indicator (AAE-HI) for remaining useful life (RUL) prediction. Accelerated degradation tests of bearings collected from PRONOSTIA were used to validate the proposed AAE-HI method. It is shown that our proposed AAE-HI can autonomously find monotonicity and trendability of features, which will capture the degradation progression from the measurement. Therefore, the performance of AAE-HI in RUL prediction is promising compared with other conventional HIs.

An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model

In practical engineering, accurate prediction of remaining useful life (RUL) is always necessary for effective preparation of engineering assets, human resources and maintenance actions. With the improvement of computing power and the passionate requirements for the high prediction accuracy of complex systems, more and more deep model-based frameworks have been developed for RUL prediction. In general, these frameworks consist of two stages: the first one is the manual operation of feature extraction and feature selection; the second one is the RUL prediction that mainly employs the recurrent deep models. However, such the frameworks do not fully take advantage of the deep models since they still rely on much prior knowledge and do not achieve the satisfied prediction performance. In this paper, a novel two stage framework with less prior knowledge, namely, end-to-end framework, is proposed to improve the forecasting performance. In our first stage, a feature pre-extraction mechanism is designed to pre-extract the low-level features in relatively high dimensional space, which requires no additional manual operations of feature fusion and feature selection in existing methods. In our second stage, adaptive transformer, a new deep model integrating the attention mechanism and the recurrent architecture, is proposed to model the relationships between these low-level features and the RULs directly, which suppresses the issue of vanishing gradients and is more suitable for representing the complex temporal degradation characteristics. Two public bearing datasets are employed to validate the effectiveness of the proposed framework in this paper. In these two case studies, some existing state-of-the-art RUL prediction approaches are fully compared, and the critical hyperparameters and components of our framework are analyzed in details. The experimental results reveal our advantage on adaptive degradation modeling and accurate RUL prediction, and help to interpret the impact of the proposed framework architecture on bearing RUL prediction.

Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

The remaining useful life (RUL) estimation of bearings is critical for ensuring the reliability of mechanical systems. Owing to the rapid development of deep learning methods, a multitude of data-driven RUL estimation approaches have been proposed recently. However, the following problems remain in existing methods: 1) Most network models use raw data or statistical features as input, which renders it difficult to extract complex fault-related information hidden in signals; 2) for current observations, the dependence between current states is emphasized, but their complex dependence on previous states is often disregarded; 3) the output of neural networks is directly used as the estimated RUL in most studies, resulting in extremely volatile prediction results that lack robustness. Hence, a novel prognostics approach is proposed based on a time–frequency representation (TFR) subsequence, three-dimensional convolutional neural network (3DCNN), and Gaussian process regression (GPR). The approach primarily comprises two aspects: construction of a health indicator (HI) using the TFR-subsequence–3DCNN model, and RUL estimation based on the GPR model. The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy. Subsequently, the 3DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs. Finally, the RUL of the bearings is estimated using the GPR model, which can also define the probability distribution of the potential function and prediction confidence. Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence–3DCNN–GPR approach. The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.

A deep attention residual neural network-based remaining useful life prediction of machinery

Remaining useful life (RUL) estimation has always been an essential task of prognostics health management (PHM). However, degradation data of machinery is seldom available, which limits the performance of data-driven models. Besides, the prediction performance of the established model may also be awfully affected by working environment of monitored machinery. To over these problem, a novel deep attention residual neural network (DARNN) is proposed by us for RUL prediction of machinery. The proposed DARNN has following advantages: (1) Representations of degradation can be effectively extracted from signals by the proposed DARNN. (2) The prediction performance and self-stability of the proposed model significantly surpassed some existing methods. Two case studies are conducted for the RUL prediction of bearings and turbofan engines respectively to verify the effectiveness of the proposed model.

A novel method for journal bearing degradation evaluation and remaining useful life prediction under different working conditions

Accurate bearing degradation performance analysis and remaining useful life (RUL) prediction are significant to prevent major accidents and economic losses in industry. Data-driven methods have emerged as reliable algorithms for RUL prediction. These existing approaches assume that the training (source) and testing (target) samples have the same probability distribution. However, the obtained run-to-failure datasets with different work conditions are usually from different domains. The distribution discrepancy between the source and target domain will reduce the accuracy of RUL prediction models when only source domain data in one working condition is trained. To solve the problem, this paper proposes a novel transfer learning method for journal bearing RUL prediction under different work conditions based on the LSTM-DNN network with domain adaptation. The multi-sensor run-to-failure datasets of journal bearings are collected and the extracted multi-sensor features are used for degradation assessment through the fuzzy c-means (FCM) clustering algorithm and the determination of degradation occurrence time (DOT). The multi-sensor feature representations and RUL values after DOT are used to validate the effectiveness of the proposed model. The results show that the proposed method has higher accuracy in journal bearing RUL prediction under different work conditions and outperforms other transfer learning approaches.

Remaining useful life estimation using deep metric transfer learning for kernel regression

Accurate estimation of remaining useful life (RUL) is indispensable for the safe operation of rotating machinery, reducing maintenance costs and unnecessary downtime. Numerous data-driven models have been reported to predict the RUL of bearings using historical data. However, it is still very challenging to predict the RUL of bearings under different operating conditions. It is necessary to propose a model which can extract domain invariant deep features and accurately predict the RUL of bearings under new operating condition. In this paper, a novel method called deep transfer metric learning for kernel regression (DTMLKR) is proposed and applied to the RUL prediction of bearings under multiple operating conditions. This method combines deep metric learning with transfer learning (TL) to solve regression problems. Case studies on the IEEE PHM Challenge 2012 dataset demonstrate the effectiveness of the proposed method. Compared with other state-of-the-art methods, the superiority of the proposed method is verified.

Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method

Remaining useful life (RUL) using exponential model (EM) prediction has been a hot research topic in the construction of prognostics health management (PHM) systems. However, in RUL prediction of rolling bearings, the EM 1) depends on the appropriate first prediction time (FPT), 2) requires reliable methods to optimize the model. Therefore, an improved EM is developed to predict the RUL of rolling bearings. Firstly, an adaptive method based on kurtosis and root mean square (RMS) of bearing vibration signals is used to determine the appropriate FPT. Secondly, gradient descent method is used to reliably optimize the EM. A commonly used bearing degradation datasets are analyzed to show the advantages of the present method. Compared with the traditional EM, the method can not only adaptively determine FPT, but also predict RUL more accurately.

Self-Attention ConvLSTM and Its Application in RUL Prediction of Rolling Bearings

Traditional long short-term memory (LSTM) neural networks generally face the challenge of low training efficiency and poor prediction accuracy for the remaining useful life (RUL) prediction due to their structure. In this study, a novel model called self-attention ConvLSTM (SA-ConvLSTM) neural network is proposed derived from ConvLSTM and a SA mechanism. First, convolution operators replace the fully connected layers inside the network structure to reduce the redundancy of the network and enhance its nonlinear modeling capability. Subsequently, a SA module is designed and embedded into the interior of the model by adaptively employing the corresponding important information to improve the prediction performance. Extensive experiments on the test rig and the actual wind farm confirmed that the developed SA-ConvLSTM has advantages over other conventional prediction methods in terms of convergence speed and prediction precision.

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 Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation

Transfer learning (TL) and domain adaptation (DA) have aroused the interest of many researchers and are used in the prognostics and health management (PHM) of bearings. However, traditional DA methods tend to lose fine-grained information when aligning the domains globally. Moreover, most DA methods are not suitable for regression tasks, such as remaining useful life (RUL) prediction. This article proposes a deep subdomain adaptive regression network (DSARN), which can align the relevant subdomains in the source and target domains. This method has good cross-domain generalization ability and thus can be used for RUL prediction of bearings under multiple operating conditions. The proposed method takes the multichannel time-frequency representation (TFR) of bearings as the input and uses DSARN to extract the domain-invariant hidden representation and to establish a mapping to RUL. The effectiveness of the proposed method is verified by case studies on bearings. A comparison with other state-of-the-art deep learning (DL) and TL methods also proves the superiority of the proposed method.

Prediction of Bearings Remaining Useful Life Across Working Conditions Based on Transfer Learning and Time Series Clustering

Recently, data-driven remaining useful life (RUL) prediction has become a promising tool in prognostics and health management for rolling bearings. In many actual applications, however, it is not easy to collect whole-life degradation data of bearings, while training with an insufficient amount of data would result in a biased RUL prediction model. If the monitoring data historically accumulated under different working conditions are introduced to facilitate model training, it probably tends to get inadequate prediction performance due to the violation of the precondition of independent and identical distribution ( ${i.i.d.}$ ). To solve this problem, a new bearing RUL prediction approach is proposed by utilizing the transfer learning strategy. First, a new time series clustering algorithm is proposed to exploit clusters of different degradation series. By integrating phase space warping and dynamic time warping, this algorithm is able to measure the similarity by using global degradation information and then get better clustering performance. Second, a new temporal domain adaptation method is proposed to obtain the pivot feature set of different bearings based on meta-degradation information which is represented by the principal curve of each cluster. Finally, support vector machine is run to establish the prediction model by means of these features, and RUL of the target bearing can be predicted via the same feature adaptation. A theoretical analysis is also provided to prove that the proposed approach has an upper bound of information loss in the transfer process. Experimental results on the IEEE PHM challenge 2012 dataset demonstrate the effectiveness of the proposed approach.

Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction

As one of the most important components of machinery, once the bearing has a failure, serious catastrophe may happen. Hence, for avoiding the catastrophe, it is valuable to predict the remaining useful life (RUL) of bearing. Health indicators (HIs) construction plays a greatly important role in the data-driven RUL prediction. Unfortunately, most of the existing HIs construction methods need prior knowledge and few of them construct HIs from raw vibration signals. For dealing with the above issues, a novel quadratic function-based deep convolutional auto-encoder is developed in this work. The raw bearing vibration signals are first preprocessed by low-pass filtering. Then the cleaned vibration signals are input into the quadratic function-based DCAE neural networks for constructing HIs of bearings. Compared with AE, DNN, KPCA, ISOMAP, PCA and VAE, it is revealed that the proposed methodology can construct a better HI from the raw bearing vibration signal in terms of comprehensive performance. Several comparative experiments have been implemented, and the results indicate that the HI constructed by quadratic function-based DCAE neural network has stronger predictive power than the traditional data-driven HIs.

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.