Remaining Useful Life Prognostics Research Articles

Optimal predictive selective maintenance for fleets of mission-oriented systems

ABSTRACT In many settings, fleets of assets must perform series of missions with in-between finite breaks. For such fleets, a widely used maintenance strategy is the fleet selective maintenance (FSM). Under resource constraints, the FSM problem selects an optimal subset of feasible maintenance actions to be performed on a subset of components to minimise the maintenance cost while ensuring high system reliability during the upcoming mission. The majority of extant FSMP models are focussed on traditional physics-based reliability models. With recent advances in Machine Learning (ML) and Deep Learning (DL) algorithms, data-driven methods have shown accuracy in predicting remaining useful life (RUL). This paper proposes a predictive FSM strategy for fleets of complex and large multicomponent systems. It relies on a concurrent ML/DL and optimisation approach where a clustering algorithm is used to determine the health states of components and a probabilistic RUL prognostics model is used for component reliability assessment. An optimisation model is developed to solve the predictive FSM problem to ensure high reliability of all systems within the fleet. An efficient two-phase solution approach is developed to solve this complex optimisation problem. Numerical experiments show the validity of the approach and highlight the improved maintenance plans achieved.

An adaptive model with dual-dimensional attention for remaining useful life prediction of aero-engine

As an indispensable part in prognostics and health management (PHM) of mechanical systems, the remaining useful life (RUL) estimation has been studied widely and intensively. Recently, with the boom in deep learning, the traditional data-driven prognostic methods like the temporal convolutional neural networks (TCNs), have made remarkable progress in the RUL estimation. However, the conventional TCNs methods do not have a well-established mechanism to parallel weigh the dual-dimensional features of multi-sensory data. Moreover, the conventional TCNs have a fixed network structure which is not flexible to learn deep temporal representations. Here, we propose an adaptive model with dual-dimensional attention for aero-engine RUL prognostics. Specifically, the dual-dimensional multi-head attention (DMHA) module is constructed to assign weights to features in the spatial and temporal dimensions, and then preforms the feature fusion. The mathematical derivations of the DMHA module parameter iterations are performed, which enhances the interpretability of the model. Next, the adaptive TCN (ATCN) is designed to further learn the time correlations of the fused features. Compared with the traditional TCN, the ATCN can adjust the network structure adaptively and extract the deep temporal representations better. Last, the prediction ability of the DMHA-ATCN is verified by conducting experiments on the C-MAPSS dataset.

MHT: A multiscale hourglass-transformer for remaining useful life prediction of aircraft engine

Remaining useful life (RUL) prediction of aircraft engines is significant in the health monitoring, operation, and maintenance of aircraft. Capturing more comprehensive device degradation trends at different time scales and extracting long-term dependencies effectively among elements in long time series are two challenges in the field of aircraft engine RUL estimation. To address the aforementioned challenges, this paper proposes a novel multiscale Hourglass-Transformer (MHT) aircraft engine RUL prognostics. Specifically, an hourglass-shaped multiscale feature extractor (HME) is designed based on one-dimensional convolutional neural network, which can scale the time sequence into multi-time scales for feature fusion. Then, a transformer network is employed to further extract features from the fused feature map and output the RUL. To enhance inter-scale data attention, a pyramid self-attention mechanism is employed in both the encoder and decoder. Finally, the superiority and effectiveness of this approach are verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Furthermore, the robustness and generalization capability of this method are further validated on New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset.

Knowledge enhanced ensemble method for remaining useful life prediction under variable working conditions

Remaining useful life (RUL) prediction is essential in enhancing the safety and reliability of rotating machinery. Deep learning techniques have been extensively researched and demonstrated promising results in RUL prediction tasks. But most existing models are designed for machinery equipment in a specific condition. In this case, a novel prediction method, knowledge-enhanced convolutional Transformer ensemble model (KE-CTEM), is proposed in this study. First, a feature extraction neural network (FENN) is introduced to extract features and transfer the working conditions information of existing datasets as knowledge to downstream RUL prediction tasks. Then, a convolutional Transformer model is leveraged to capture the input data degradation patterns and predict RUL values. Finally, knowledge-enhanced strategy and ensemble strategy are proposed to enhance the robustness of the model and improve the prediction accuracy.To verify the practicality and effectiveness of the proposed method, run-to-failure data of bearings from PRONOSTIA platform are utilized for RUL prognostics. Compared with several representative and state-of-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method. And ablation study indicates the high efficiency and robustness of each module within the proposed model. Compared with representative RUL prediction methods, the proposed KE-CTEM demonstrates superior performance in terms of RMSE and MAPE with a reduction of 32.0% and 16.2%, respectively.

Remaining useful life with self-attention assisted physics-informed neural network

Remaining useful life (RUL) prediction as the key technique of prognostics and health management (PHM) has been extensively investigated. The application of data-driven methods in RUL prediction has advanced greatly in recent years. However, a large number of model parameters, low prediction accuracy, and lack of interpretability of prediction results are common problems of current data-driven methods. In this paper, we propose a Physics-Informed Neural Networks (PINNs) with Self-Attention mechanism-based hybrid framework for aircraft engine RUL prognostics. Specifically, the self-attention mechanism is employed to learn the differences and interactions between features, and reasonably map high-dimensional features to low-dimensional spaces. Subsequently, PINN is utilized to regularize the end-to-end prediction network, which maps features to RUL. The RUL prediction framework termed AttnPINN has verified its superiority on the Commercial Modular AeroPropulsion System Simulation (C-MAPSS) dataset. It achieves state-of-the-art prediction performance with a small number of parameters, resulting in computation-light features. Furthermore, its prediction results are highly interpretable and can accurately predict failure modes, thereby enabling precise predictive maintenance.

A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems

Empowered by the ubiquitous sensing infrastructure, predictive maintenance (PdM) has received increasing attention. However, designing predictive maintenance for multiple systems remains a challenging problem as it requires effective integration of the prediction and scheduling under uncertainty and constraints. This paper proposes a dynamic PdM approach for a fleet of multi-component systems, which integrates deep learning-based (DL-based) probabilistic remaining useful life (RUL) prognostics into maintenance planning. First, a DL-based framework is designed for obtaining predictive distributions of RULs, and a probabilistic gated recurrent unit model is proposed for time series data. The Gaussian distribution is used to model prediction uncertainties, and the negative log-likelihood loss is used for model training, which inclines to emphasize the late degradation period. Then, an integer program is established and embedded in a rolling horizon approach for maintenance optimization. The concept of wasted residual values is proposed to quantify the impact of early retirement of components on total costs, which enables customized group maintenance planning under different cost settings. The applicability of the overall approach is illustrated for a fleet of aircraft based on a practical dataset. The experimental results show that our approach can provide accurate RUL prognostics and schedule maintenance flexibly to reduce costs.

Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability.

A Comparative Study of the Kalman Filter and the LSTM Network for the Remaining Useful Life Prediction of SOFC

The solid oxide fuel cell (SOFC) system is complicated because the characteristics of gas, heat, and electricity are intricately coupled. During the operation of the system, problems such as frequent failures and a decrease in the stack’s performance have caused the SOFC system to work less well and greatly shortened the SOFC’s practical life. As such, it is essential to accurately forecast its remaining useful life (RUL) to make the system last longer and cut down on economic losses. In this study, both model-based and data-driven prediction methods are used to make predictions about the RUL of SOFC. First, the linear degradation model of the SOFC system is established by introducing degradation resistance as the index of health status. Using the Kalman filtering (KF) method, the health status of SOFC is evaluated online. The results of the health state estimation indicated that the KF algorithm is accurate enough to provide a good basis for the model-based RUL prediction. Then, a long short-term memory (LSTM) network-recursive (data-driven) method is presented for RUL prognostics. The multi-step-ahead recursive strategy of updating the network state with actual test data improves the prediction accuracy. Finally, a comparison is made between the LSTM network prediction approach suggested and the model-based KF prognostics. The results of the experiments indicate that the LSTM network is more suitable for RUL prediction than the KF algorithm.

Deep Bidirectional Recurrent Neural Networks Ensemble for Remaining Useful Life Prediction of Aircraft Engine.

Remaining useful life (RUL) prediction of aircraft engine (AE) is of great importance to improve its reliability and availability, and reduce its maintenance costs. This article proposes a novel deep bidirectional recurrent neural networks (DBRNNs) ensemble method for the RUL prediction of the AEs. In this method, several kinds of DBRNNs with different neuron structures are built to extract hidden features from sensory data. A new customized loss function is designed to evaluate the performance of the DBRNNs, and a series of the RUL values is obtained. Then, these RUL values are reencapsulated into a predicted RUL domain. By updating the weights of elements in the domain, multiple regression decision tree (RDT) models are trained iteratively. These models integrate the predicted results of different DBRNNs to realize the final RUL prognostics with high accuracy. The proposed method is validated by using C-MAPSS datasets from NASA. The experimental results show that the proposed method has achieved more superior performance compared with other existing methods.

Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns

Recent advances in multivariate data fusion technology have promoted the applications of neural network-based models for remaining useful life (RUL) prediction. However, the interpretability of these models is usually poor since they are developed in a black-box manner. It is difficult to use them in engineering systems with multiple failure modes (FMs) under various operation conditions (OCs). This work proposes an adaptive deep learning-based RUL prediction framework with FM recognition. First, a FM recognizer fusing physics-informed FM classifier with deep convolutional neural networks (DCNN) is developed, which improves the interpretability and the accuracy of the recognition model. Then, a framework which can adaptively train models and select them for RUL prediction according to FM recognition results is presented. An OC-based smoothing technique is proposed to improve the RUL prediction accuracy and robustness. Extensive experiments based on turbofan datasets are conducted to validate the effectiveness of the proposed framework. The results show that the RUL prediction accuracy is improved by 7% under the proposed framework when compared with other methods. It proves the performance gains of the proposed framework by incorporating prior FM recognition with RUL prediction. It also provides insights for RUL prognostics subject to distinct FMs and OCs.

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage.

Remaining useful life prognostics of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression

Lithium battery has been widely applied as new energy to cope with pressures in both form environment and energy. The remaining useful life (RUL) prognostics of lithium-ion batteries have become more critical. Convenient battery life prediction allows early detection of performance deficiencies to help maintain the battery system promptly. This paper proposes a RUL prognostics model of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression. First, a new sampling rule is used to reconfigure the coordinates of degradation data of new batteries and truncated similar batteries. Then, the relationship between similar and new lithium-ion batteries is established by using the reconfiguration data. Moreover, a new RUL prognostics model based on a coordinate reconfiguration of degradation trajectory and multiple linear regression is established by considering the influence of time-varying factors, which can improve prediction accuracy with small sample data and significantly reduce product development time and cost.

Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder

Most Remaining Useful Life (RUL) prognostics are obtained using supervised learning models trained with many labelled data samples (i.e., the true RUL is known). In aviation, however, aircraft systems are often preventively replaced before failure. There are thus very few labelled data samples available. We therefore propose a Long Short-Term Memory (LSTM) autoencoder with attention to develop health indicators for an aircraft system instead. This autoencoder is trained with unlabelled data samples (i.e., the true RUL is unknown). Since aircraft fly under various operating conditions (varying altitude, speed, etc.), these conditions are also integrated in the autoencoder. We show that the consideration of the operating conditions leads to robust health indicators and improves significantly the monotonicity, trendability and prognosability of these indicators. These health indicators are further used to predict the RUL of the aircraft system using a similarity-based matching approach. We illustrate our approach for turbofan engines. We show that the consideration of the operating conditions improves the monotonicity of the health indicators by 97%. Also, our approach leads to accurate RUL estimates with a Root Mean Square Error (RMSE) of 2.67 flights only. Moreover, a 19% reduction in the RMSE is obtained using our approach in comparison to existing supervised learning models.

Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics

The increasing availability of sensor monitoring data has stimulated the development of Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However, existing studies focus either on RUL prognostics only, or propose maintenance planning based on simple assumptions about degradation trends. We propose a framework to integrate data-driven probabilistic RUL prognostics into predictive maintenance planning. We estimate the distribution of RUL using Convolutional Neural Networks with Monte Carlo dropout. These prognostics are updated over time, as more measurements become available. We further pose the maintenance planning problem as a Deep Reinforcement Learning (DRL) problem where maintenance actions are triggered based on the estimates of the RUL distribution. We illustrate our framework for the maintenance of aircraft turbofan engines. Using our DRL approach, the total maintenance cost is reduced by 29.3% compared to the case when engines are replaced at the mean-estimated-RUL. In addition, 95.6% of unscheduled maintenance is prevented, and the wasted life of the engines is limited to only 12.81 cycles. Overall, we propose a roadmap for predictive maintenance from sensor measurements to data-driven probabilistic RUL prognostics, to maintenance planning.

Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics

Predictive maintenance, such as remaining useful life (RUL) prognostics, requires precise long time-series forecasting, which demands a higher predictive capability of data-driven models. Nevertheless, the typical convolution and recurrent frameworks are still inadequate in the feature extraction and temporal complexity analysis, which makes them difficult to efficiently capture the precise long-term dependency coupling. Recent research has demonstrated the potential of Transformer-based framework to improve the prediction capability by the massive success in sequence processing. Inspired by the above, this paper proposes an efficient end-to-end Temporal Flow Transformer (TFT) for RUL prognostics of rolling bearings. Its main framework is composed of multi-layer encoders, which can directly extract effective degradation features from the time-frequency representations of raw signals, with two distinctive characteristics: (1) Specially designed multi-head probsparse self-attention mechanism can effectively highlight the dominant attention, which makes the TFT have considerable performance in reducing the computational complexity of extremely long time-series; (2) The TFT trained by knowledge-induced distillation strategy can significantly improve its domain adaptability, making it possible to achieve accurate RUL prediction under cross-operating conditions. Extensive experiments on two life-cycle bearing datasets indicate that the TFT greatly outperforms the existing state-of-the-art methods and provides a new solution for RUL prognostics.

Remaining-Useful-Life prognostics for opportunistic grouping of maintenance of landing gear brakes for a fleet of aircraft

Several studies have proposed Remaining-Useful-Life (RUL) prognostics for aircraft components in the last years. However, few studies focus on integrating these RUL prognostics into maintenance planning frameworks. This paper proposes an optimization model for opportunistic maintenance scheduling of aircraft components that integrates RUL prognostics and that groups the maintenance of these components to reduce costs. We illustrate our approach for the maintenance of a fleet of aircraft, each equipped with multiple landing gear brakes. RUL prognostics for the landing gear brakes are obtained using a Bayesian regression model. Based on these RUL prognostics, we group the replacement of brakes using an integer linear program. As a result, we obtain a cost-optimal RUL-driven opportunistic-maintenance schedule for the brakes of a fleet of aircraft. Compared with traditional maintenance strategies, our approach leads to a reduction of up to 20% of the total maintenance costs.

Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.

Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture

Remaining useful life (RUL) estimation has been intensively studied, given its important role in prognostics and health management (PHM) of industry. Recently, data-driven structures such as convolutional neural networks (CNNs), have achieved outstanding RUL prediction performance. However, conventional CNNs do not include an adequate mechanism for adaptively weighing input features. In this paper, we propose a double attention-based data-driven framework for aircraft engine RUL prognostics. Specifically, a channel attention-based CNN was utilized to apply greater weights to more significant features. Next, a Transformer was used to focus attention on these features at critical time steps. We validated the effectiveness of the proposed framework on benchmark datasets for aircraft engine RUL estimation. The experimental results indicate that the proposed double attention-based architecture outperformed the existing state-of-the-art (SOTA) algorithms. The double attention-based RUL prediction method can detect the risk of equipment failure and reduce loss.

Remaining Useful Life Prediction of Machinery: A New Multiscale Temporal Convolutional Network Framework

With the rapid development of deep learning (DL) techniques, data-driven models have been increasingly used in remaining useful life (RUL) prediction, in which convolution neural network (CNN)-based RUL prognostics models have received special attention. However, there are still two main issues that need to be addressed: (1) Traditional CNN is not suitable to extract the time-sequence characteristics from the long-term historical signals. (2) The receptive field range of convolution operation is fixed, thus only learning the feature information at a specific scale, which is insufficient for complex feature extraction. To address these two issues, a multi-scale temporal convolutional network (MsTCN) which has powerful time-sequence characteristics is proposed for RUL prediction in this paper. The MsTCN adopts the temporal convolutional network (TCN) framework, which is good at extracting time-sequence information. Based on this, a new multi-scale dilated causal convolution residual block (MsDCCRB) is developed to constitute the RUL prognostics model, where multiple dilated convolutions (DCs) are based on different dilation factors are put on each layer in parallel. Furthermore, the squeeze-and-excitation (SE) unit is embedded into the MsDCCRB to adaptively recalibrate the sequence feature responses and enhance the representation learning ability. Through stacking multiple MsDCCRBs, the historical condition monitoring data can be fed directly into the proposed model to realize the high-level representations of RUL estimation. Finally, the proposed approach is validated with the accelerated whole-life degradation dataset of rolling element bearings (REBs). The experimental results exhibit the proposed MsTCN achieves a higher RUL prediction accuracy, which is superior to some state-of-the-art data-driven prognostics methods.

Model-based Remaining-Useful-Life prognostics for aircraft Cooling Units

Prognostics for the Remaining-Useful-Life (RUL) of aircraft components are crucial to support efficient aircraft maintenance planning and, in particular, to limit unscheduled maintenance due to unexpected component failures. As such, predictive methods for the RUL of aircraft components are increasingly a priority for aircraft Maintenance, Repair and Operations (MROs). In this paper we develop model-based RUL prognostics for aircraft Cooling Units using operational data recorded during the flights of several wide-body aircraft. A Cooling Unit is a vapor cycle refrigeration unit consisting of a condenser, a flash tank, an evaporator and a compressor. After some time of usage, the filter of these Cooling Units is clogged with burned oil, moist and sludge from the compressor. This accelerates the wear of the components. Long time utilization of these components in these conditions leads to a failure. To model the degradation of the Cooling Units, we use an exponential functional form for the degradation. Together with sequential Monte Carlo methods, we estimate the probability distribution of the RUL of these components. The exponential functional form of the degradation is based on the fact that the cumulative damage in the components has an effect on the degradation rate. It has been shown that an exponential model is a good approximation for non-linear degradation processes like corrosion, bearing degradation, or deterioration of LED lighting. In fact, the Cooling Units can also be seen as subject to corrosion and accelerated wear. We evaluate our RUL prognostics for various prediction horizons, i.e., at 30, 20 and 10 flight cycles before failure. The results show that our proposed methodology is able to estimate the RUL of the Cooling Units well, and that the uncertainty associated with the prognostics decreases as the prediction horizon decreases, i.e., as the components approach failure. The choice of the prediction horizon is relevant from the point of view of MROs, which re-evaluate periodically their aircraft maintenance schedules. In practice, regular maintenance checks are scheduled every two weeks. Having accurate RUL prognostics over such time horizons enables the maintenance planners to better plan tasks, limiting unscheduled failures. In addition, the fact that we estimate the uncertainty associated with the RUL prognostics enables the maintenance planners to prioritize the maintenance of the components. Overall, our results provide support for maintenance planners to make informed and efficient maintenance schedules.