Online Remaining Useful Life Prediction Research Articles

A remaining useful life prediction framework with adaptive dynamic feedback

Remaining useful life (RUL) prediction of components is a crucial aspect of mechanical systems prognostic and health management. However, during the online RUL prediction of components, the existing method cannot use the prediction results for feedback and optimize the prediction model. To address this issue, this paper proposes a framework for RUL prediction with adaptive dynamic feedback. First, a feedback multi-feature fusion (FMFF) method is designed. It introduces a series of non-exceeding threshold positive constants, that is fraction thresholds (FT), to perform division of the degradation state dimension. Besides, the first hitting time (FHT) from the current moment to FT is the fraction remaining useful life (FRUL). It is used to construct and update the fusion factors. Then, the feedback multi-model selection (FMMS) method is proposed for adaptive feedback and to select the degradation model based on the FRUL prediction results in different stages. The probability density function of RUL based on the FHT is calculated based on the FMMS method to realize the RUL prediction of the component. Finally, the effectiveness and superiority of the proposed framework are validated based on XJTU-SY dataset and run-to-failure test.

Challenges on prognostics and health management for wind turbine components

This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

Li-ion battery capacity prediction using improved temporal fusion transformer model

Lithium-ion (Li-ion) batteries have near-zero energy emissions and provide power to various devices, such as automobiles and portable equipment. The strategy predicts the capacity of Li-ion in advance and can also help arrange maintenance tasks. To improve state of health (SOH) and remaining useful life (RUL) prediction accuracy, we propose an improved temporal fusion transformer (ITFT) method based on bidirectional long short-term memory (Bi-LSTM) encoder-decoder layer replacing the long short-term memory for probabilistic online RUL prediction. A novel interpretable hyperparameter tuning method called Bayesian optimization based on tree-structure Parzen estimator (TPE) is coupled with a unique ITFT model to improve RUL prediction accuracy. Furthermore, we consider the effects of keen-onset to establish the starting point of our training. The root mean square error for four batteries using the proposed model for the test data are 0.0018, 0.0019, 0.0013, and 0.0025, respectively, which outperforms the other models, with an improvement accuracy rate above 25%. The proposed model SOH results indicate that our proposed approach outperforms some previously published methods. Our online RUL prediction demonstrates relative errors of 1.18%, 1.54%, 1.06%, 2.70%, 0.67%, 2%, 3.90%, 0%, and 3.08% for nine batteries, respectively. These results for SOH and RUL predictions emphasize the excellent performance of our proposed method.

Unsupervised incremental transfer learning with knowledge distillation for online remaining useful life prediction of rotating machinery

Online remaining useful life (RUL) prediction has been solved by deep transfer learning, but is still with challenges as follows: (1) Incomplete and unlabeled data under actual operation; (2) Condition monitoring data is streaming with unknown distribution; and (3) The distribution of degradation data is variable. To solve them, an unsupervised incremental transfer learning approach with knowledge distillation (KD) is proposed. First, a time series recursive prediction model is built to generate pseudo labels. Second, an online KD network is constructed to realize unsupervised domain adaptation. Finally, an incremental updating mechanism is designed in the online KD network for online RUL prediction with the pseudo labels. Comparative experiments on the IEEE PHM Challenge 2012 rolling bearing dataset show that the proposed method, being computationally inexpensive, can effectively achieve dynamic prediction only with sequentially-collected data, which can provide an effective RUL prediction solution for rotating machinery under an open environment.

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation

Accurate remaining useful life (RUL) prediction technique matters in lithium-ion battery use, optimization, and replacement. This article presents an RUL prediction method combining the sliding window (SW) technique and Box-Cox transformation (BCT). This method achieves online RUL prediction with acceptable accuracy, is independent of offline training data, and only brings a low computational burden. The SW technique is employed for gathering a certain amount of capacity data, which is subsequently transformed using BCT-related techniques to construct a capacity degradation model. The identified model is then extrapolated to predict battery RUL, and the prediction uncertainties are calculated through Monte Carlo (MC) simulation. A simple implementation shows that this hybrid method outperforms the history-based polynomial and BCT methods in battery RUL prediction. Given the segmented capacity degradation trend, a constraint can be imposed on the model parameter for optimization purposes. Experimental results demonstrate that the optimized method obtains lower root-mean-square errors (RMSEs) of RUL predictions during the last 20 % of the battery lifetime than the original one, and the precise RULs are predicted with standard deviations mainly within [1, 10] cycles.

A two-stage online remaining useful life prediction framework for supercapacitors based on the fusion of deep learning network and state estimation algorithm

Supercapacitors have been widely used in many fields. The safe and stable operation of supercapacitors requires accurate remaining useful life (RUL) prediction. This paper proposes a two-stage online RUL prediction framework based on the bidirectional long short-term memory (BiLSTM) network and the H∞ observer. In Stage 1, the BiLSTM network as well as the Bayesian Optimization algorithm is used to estimate capacitance. The Bayesian Optimization (BO) algorithm aims to optimize the hyperparameters of the BiLSTM network. In Stage 2, the estimated capacitance is handled by the moving average filter (MAF) to alleviate short-term fluctuations. The double exponential model is employed to describe the degradation trajectory, and the model is iteratively updated by the H∞ observer with the estimated capacitance as the measurement. Experiments are implemented to verify the validation of the proposed framework. The results indicate that the BiLSTM network can explain more than 99% variation of capacitance. The deviation of RUL prediction is less than 6% in most cases, and it is even less than 2.5% in late phase. The proposed framework takes the advantages of online deployment, and achieves competitive prediction accuracy when compared with offline methods.

Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism

As battery management systems are widely used in industrial applications, it is important to accurately predict the online remaining useful life (RUL) of batteries. Due to side reactions, the battery will continue to decline in capacity and internal resistance throughout its life cycle. Additionally, battery systems require reliable and accurate battery health diagnostics and timely maintenance and replacement. To obtain accurate RUL prediction, we propose a bidirectional long short-term memory with attention mechanism (Bi-LSTM-AM) model to predict online RUL by continuously updating the model parameters. In this study, normalized capacity was used as state of health (SOH). Multi-step ahead prediction using a sliding window method was used to obtain the SOH estimates. Six cylindrical and prismatic lithium-ion (Li-ion) batteries were used to evaluate the performance of the proposed model. Using our online RUL prediction model, the relative errors for the six Li-ion batteries are 0.57%, 0.54%, 0.56%, 0%, 1.27% and 1.41%, respectively. To evaluate the reliability of the proposed model, the prediction interval for the RUL prediction is also provided using the Monte Carlo dropout approach.

A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump

An efficient Remaining Useful Life (RUL) prediction method is one of the most important features of a condition-based maintenance system. A running machine’s RUL prognosis in its real-time is a challenging task, especially when there is no historic failure data available for that particular machine. In this paper, an online RUL of an in-operation industrial slurry pump having no historical failure data has been predicted. At first, the available raw vibration datasets were filtered out for valid datasets. The obtained valid datasets were utilized for constructing the Health Degradation Trends (HDTs) using principal component analysis and a moving average method. Then, a novel procedure for automatically selecting the HDT’s data points for initiating the iteration process of prediction was formulated. Afterward, a hybrid deep LSTM model embedded with a smart learning rate mechanism was developed for estimating the online RUL using the selected points of HDTs. The online RUL prediction results produced by the developed model were quite satisfactory when they were compared with other online RUL prediction methods.

Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

Meta-modeling of heterogeneous data streams: A dual-network approach for online personalized fault prognostics of equipment

In fault prognosis, the individual heterogeneity among degradation processes of equipment is a critical problem that decreases the reliability and stability of prognostic models. The presence of the diversity of degradation mechanisms, along with the complex temporal nature of multivariate measurements of equipment, make the existing approaches difficult to forecast the trend of health status and predict the Remaining Useful Life (RUL) of equipment. To resolve this problem, this article proposes a dual-network approach for online RUL prediction. The proposed approach predicts the RUL by constructing a recurrent neural network (RNN) and a Feedforward Neural Network (FNN) from the degradation measurements and failure occurrence data of equipment. The RNN is used to predict the evolution of degradation measurements, whereas the FNN is used to determine the failure occurrence based on the predicted measurements. Considering the individual heterogeneity problem, a novel meta-learning procedure is proposed for network training. The main idea of the meta-learning approach is to train two network generators to capture the average behavior and variation of equipment degradation, and generate dual networks dynamically tailored to different equipment in the online RUL prediction process. Numerical studies on a simulation dataset and a real-world dataset are performed for performance evaluation.

Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

Data-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

Accurate prediction of the remaining useful life (RUL) in Lithium-ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)- based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non-linear characteristics and non-Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16.

Prediction of remaining useful life for lithium-ion battery with multiple health indicators

Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

A Health Index Construction Framework for Prognostics Based on Feature Fusion and Constrained Optimization

The actual health state of a system is difficult to measure in online applications. Hence, features extracted from the sensor data are usually fused as a composite health index (HI) to represent the system status. If the HI shows explicit increasing or decreasing trend, prognostic methods are applied to extrapolate the future degradation and predict the remaining useful life (RUL). However, the practical requirements of prognostics for HI construction are often not considered. Thus, the advanced index may not capture the fault progression well, which would lead to inaccurate predictions. In this article, an HI construction frame-work based on feature fusion and constrained optimization is proposed. Multiple features are fused with weighted sum and nonlinear fusion functions. Three desired properties of HI for prognostics are considered to build an optimization model of feature weights. To solve this multi-variable model effectively, a self-adaptive differential evolution (SADE) algorithm is proposed. Then, a state-space model based on Wiener process is applied to the constructed HIs for online RUL prediction. To evaluate the performance of the HIs, a consistency-based metric is developed. Illustrative examples using two industrial datasets demonstrate the efficiency and adaptability of the proposed methods.

A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction

For the close relationship with safety, surface temperature has been a hot topic in the research of lithium-ion batteries. Studying surface temperature changes can ensure safety and provide information for degradation estimation. However, there are few studies on obtaining degradation information from surface temperature. In this paper, a new health indicator (HI) is proposed to predict the remaining useful life (RUL) of lithium-ion batteries from the discharge surface temperature, which is convenient for real-time measurement and online estimation. First, according to the actual trend of surface temperature, an exponential model is used to fit the surface temperature from 1000 s to the end of the discharge. Then, a new HI is extracted from the model parameters, which indicates the change rate of surface temperature. Pearson and Spearman correlation analysis verified that HI is related to battery capacity degradation. What's more, a comparison of the new HI and other types of HIs is performed. Finally, a method combining HI and relevance vector machine (RVM) is proposed for online RUL prediction. Predictions have been made for different algorithms and starting points, the results show that the new HI is effective for degradation modeling. And the RUL prediction error is less than 5 cycles for 5#, 6# and 7# batteries.

Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans

The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.

Two-stage physics-based Wiener process models for online RUL prediction in field vibration data

Due to most failure mechanisms, such as fatigue crack growth and fatigue spall, the degradation process of rotating machinery commonly exhibits two-stage features in engineering practice. Other minor factors are also the key issues affecting the health evolution process, including the component structure, assembly accuracy, and working environment. Ignoring such a mechanism may lead to imprecise in degradation modeling, life prognostic, and ultimately lead to safety risk. Besides, achieving high accuracy of prognostic emphasizes the influence of random effect in the degradation process. The contribution of this study lies in addressing this issue by proposing two-stage physics-based Wiener process models integrating: (a) fatigue crack mechanism and crack growth law, and (b) other minor factors. A general prognostic framework is formulated by jointly employing the online change point detection, parameter estimation, and remaining useful life (RUL) prediction, which has good statistic inference and applicability in two general nonlinear systems, i.e., power-law and exponential-law. A joint implement of offline two-step parameter estimation method and the online Bayesian update method is executed, making full advantage of historical and in-service data, based on which the RUL prediction transcends into an imperative PHM module. A practical case study on the vibration dataset of wheel treads demonstrates the practically implement ability of the proposed method in achieving high accuracy of RUL prediction.

Joint Modeling of Degradation and Lifetime Data for RUL Prediction of Deteriorating Products

Degradation is one of the major root causes of system failure. In some applications, the degradation levels are different upon failure, in which the fixed failure threshold assumption commonly adopted in the degradation literature may not hold. This article tackles the difficulty by jointly analyzing the system degradation and the lifetime data, which enables the corresponding remaining useful life (RUL) prediction. We treat the degradation level as a multiplicative time-varying covariate of the system hazard rate, where a random-effects Wiener process is adopted to model the degradation process. The model parameters are estimated under a Bayesian framework, and we also develop a particle filter method to update the estimates when new data are available. This makes the proposed model be able to realize online RUL prediction based on the in-situ system health state signals. Through case studies on lead-acid batteries and digital communication systems, the proposed model is shown to outperform existing methods in terms of the RUL prediction accuracy.

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations.

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.