Prognostics Research Articles

Remaining Useful Life Prediction based on Multisource Domain Transfer and Unsupervised Alignment

Transfer learning （TL） enhances remaining useful life (RUL) predictions by addressing data scarcity and operational challenges. Nonetheless, when a significant disparity in degradation data distribution exists between source and target domains, single-source domain TL may lead to misleading or negative transfer. Multisource domain TL partially mitigates these issues but fails to account for substantial discrepancies in feature-label correlations, impairing RUL prediction accuracy. To cope with this problem, we propose a multisource domain unsupervised adaptive learning method powered by a temporal convolutional network. Using a multilinear conditioning strategy, we combine degradation data and subregion labels to construct input characteristics for the domain discriminator. Additionally, we design a feature extractor that produces label-related features invariant across domains, thereby enhancing prediction precision. We evaluate our method using the publicly available C-MAPSS degradation dataset, demonstrating its effectiveness through a case study and ablation experiments.

From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness.

Bearing life prediction method based on novel health indicators and improved similarity curve matching

Abstract To tackle the issues of low accuracy in similarity curve matching and the challenges in measuring the similarity of degraded curve segments in rolling bearing life prediction methods, we propose a novel bearing remaining useful life (RUL) prediction method utilizing new health indicators and improved similarity curve matching techniques. Initially, time and frequency domain features are extracted from the bearing vibration signals. Subsequently, the multi-dimensional features are meticulously screened using monotonicity, Spearman's correlation, and uncertainty metrics to construct the health indicator (HI) and the health indicator library for the bearing. Subsequently, the support vector data description (SVDD) combined with the PID search algorithm is employed to ascertain the initial prediction time of the bearing and to construct the corresponding health indicator library. Following this, both global and local feature capture are conducted using dual dynamic time warping (DDTW) in conjunction with a sliding window to predict the remaining service life of rolling bearings.
The proposed method is validated on the PHM2012 and XJTU-SY datasets and benchmarked against the latest research. The results demonstrate that our method significantly enhances the prediction accuracy of the remaining useful life of bearings, underscoring the effectiveness and superiority of the proposed approach.and superiority of the proposed approach.

Uncertainty-Aware Prediction of Remaining Useful Life in Complex Systems

Accurate prediction of the remaining useful life (RUL) of industrial systems is critical to ensuring smooth operation and safety. Various prognostic methods have been developed, but significant challenges remain for field applications. While many methods may achieve high accuracy, they often fall short in quantifying the uncertainty of their predictions. Without uncertainty quantification, it is difficult to assess the confidence level of the prognostic results. Therefore, it is essential to transparently present the uncertainty levels in the predicted results. This Ph.D. project aims to develop novel uncertainty-aware methods for RUL prediction of complex systems. The project will address the following situations where it is more and more uncertain: (a) propose a general framework for data-driven RUL methods to quantify uncertainty and generate adaptive confidence intervals under a single fault mode and a single operating condition; (b) consider both epistemic and aleatoric uncertainties in scenarios with multiple fault modes and multiple operating conditions and then calibrate uncertainty to enhance their accuracy; (c) explore how to predict RUL and quantify uncertainty when there are no run-to-failure data and RUL labels in practice; (d) handle uncertainty propagation from the component level to the system level. Through this research, the project will provide more reliable and comprehensive solutions for RUL prediction in complex systems.

Graph Neural Network Approach to System-Level Health Index and Remaining Useful Life Estimation

Current methods for predicting health index and remaining useful life (RUL) in complex systems struggle to account for performance dependencies between components, leading to inaccurate system-level estimates. This research proposes a novel approach utilizing graph neural networks (GNNs) to improve system-level health index and RUL estimation. GNNs excel at capturing complex interdependencies within a system, making them ideal for this task. The proposed methodology is designed for systems with synchronously sampled process data. To illustrate the application of the proposed approach, we will use the Condensate Extraction Subsystem (CES) of a nuclear power plant (NPP) as a case study. Sensor data like temperature, pressure, and flow rates will be used to train GNNs to predict the overall health and RUL of the CES over time. To evaluate the effectiveness of GNNs, a custom NPP simulator will be used to model the CES under various realistic fault modes across a variety of components. The GNN's performance will be verified and its robustness will be tested under diverse scenarios. This research aims to demonstrate the effectiveness and resilience of GNNs for system-level prognostics. By providing valuable insights for maintenance decision-making, this approach can enhance operational reliability and safety in complex engineering systems. The proposed framework has the potential to be applied across various industries, leading to advancements in predictive maintenance practices.

Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.

Remaining Useful Life Prognostics of Rolling Element Bearings Based on State Estimation Techniques

Rolling element bearings (REBs) are key components in rotating machines. 40% of the failures in electrical motors occur due to bearing faults. Consequently, monitoring the health stage and estimating the remaining useful life (RUL) of the REBs is essential. Additionally, maintenance of rotating machines can be scheduled based on the RUL estimation, which will mitigate potential time wasting, economic losses and hazards. In this summary, several issues that exist in current research are highlighted. Then a series of preliminary explorations have been performed, and some results have been already obtained. Finally, a systematic methodology is expected to be proposed by combining the state estimation techniques and physical models to facilitate the development of the PHM.

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Estimation of the Remaining Useful Life of Axially Loaded Members Through Nested Least-Square Support Vector Regression

ABSTRACT Prognosis and Health Management (PHM) is crucial for ensuring the reliable operation of structural components, such as bracing members. Despite this, limited study exists for the RUL estimation of civil engineering components. This paper presents a nested framework utilizing the Least Square Support Vector Regression (LS-SVR) for accurately predicting the Remaining Useful Life (RUL) of axially loaded members. The framework addresses the challenges of sparse data and multidimensional damage-sensitive features (DSFs) by employing a two-stage process. In the first stage, DSFs are forecasted using one-dimensional mapping, providing essential inputs for the second stage, where axial stiffness degradation is predicted using LS-SVR. The proposed framework is validated through an experimental study on axially loaded members subjected to low-cycle fatigue. To enhance model performance, simulated data generated from an OpenSees model is combined with the experimental data. The nested LS-SVR framework demonstrates superior performance compared to traditional methods, achieving high accuracy in RUL estimation as evidenced by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The framework’s ability to forecast axial stiffness degradation, a critical indicator of structural health, makes it a valuable tool for real-time assessment of structural component RUL. By integrating this framework with real-time monitoring systems, proactive maintenance strategies can be implemented, leading to safer and more efficient structural health management.

Prediction of Lithium-Ion Battery Health Using GRU-BPP

Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions.

Data and model synergy-driven rolling bearings remaining useful life prediction approach based on deep neural network and Wiener process

Abstract Various Remaining Useful Life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling bearing. Hybrid methods that integrate the advantages of model-based and data-driven approaches have garnered significant attention. However, the effective integration of the two methods to address the randomness in rolling bearing full lifecycle processes remains a significant challenge. To overcome the challenge, this paper proposes a data and model synergy-driven RUL prediction framework that includes two data and model synergy approaches. Firstly, a convolutional stacked bidirectional long and short-term memory network with temporal attention mechanism is established to construct health index. The RUL prediction is achieved based on HI and polynomial model. Secondly, a three-phase prediction model based on the Wiener process is established by considering the evolution law of different degradation phases. Then, two synergy methods are designed. Strategy 1: HI is adopted as the observation value for online updating of physics degradation model parameters under Bayesian framework, and the RUL prediction results are obtained from the physics degradation model. Strategy 2: The RUL prediction results from the data-driven and physics-based model are weighted linearly combined to improve the overall prediction accuracy. The effectiveness of the proposed model is verified using two bearing full lifecycle datasets. The results indicate that the proposed approach can accommodate both short-term and long-term RUL predictions, outperforming state-of-the-art single models.

Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines

Abstract Accurate remaining useful life (RUL) prediction of aero-engines through condition monitoring (CM) data is of great significance for flight reliability and safety. Although deep learning (DL)-based approaches have been widely considered, individual DL models suffer from significant stochasticity and limited generalizability when predicting the RUL. To solve this issue, a novel multi-head attention-based variational autoencoders (MHAT-VAEs) ensemble model is proposed. Two distinct MHAT-VAEs are designed, employing linear and convolutional operations to capture global and temporal compressed representations of the CM data. Additionally, a dual-level ensemble strategy is introduced to adaptively fuse the outputs of the two base learners. A hyperparameter optimization method is also implemented to further enhance the efficiency and performance of the base learners. The effectiveness of the proposed method is validated using the C-MAPSS and N-CMAPSS datasets, with experimental results showing that it outperforms state-of-the-art approaches.

Dynamic graph spatial-temporal dependence information extraction for remaining useful life prediction of rolling bearings

As a powerful tool for learning high-dimensional data representation, graph neural networks (GNN) have been applied to predict the remaining useful life (RUL) of rolling bearings. Existing GNN-based RUL prediction methods predominantly rely on constant pre-constructed graphs. However, the degradation of bearings is a dynamic process, and the dependence information between features may change at different moments of degradation. This article introduces a method for RUL prediction based on dynamic graph spatial-temporal dependence information extraction. The raw signal is segmented into multiple periods, and multiple features of each period data are extracted. Then, the correlation coefficient analysis is conducted, and the feature connection graph of each period is constructed based on different analytical results, thereby dynamically mapping the degradation process. The graph data is fed into graph convolutional networks (GCN) to extract spatial dependence between the graph node features in different periods. To make up for the shortcomings of GCN in temporal dependence extraction, the TimesNet module is introduced. TimesNet considers the two-dimensional changes of time series data and can extract the temporal dependence of graph data within and between different time cycles. Experimental results based on the PHM2012 dataset show that the average RUL prediction error of the proposed method is 17.4%, outperforming other comparative methods.

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

Data-Driven Remaining Useful Life Estimation

This paper explores the development and application of data-driven prognostic models for estimating the Remaining Useful Life (RUL) of Nuclear Power Plant (NPP) condensers experiencing tube fouling. Due to the unavailability of run-to-failure industry sensor data, we utilized simulated data generated by the Asherah Nuclear Power Plant Simulator (ANS), initially designed by the International Atomic Energy Agency (IAEA) and programmed in Simulink for cyber security simulations. ANS's adaptability allows it to simulate Pressurized Water Reactor (PWR) behaviors given a time series of operating conditions and to introduce degradation modules to mimic fouling effects. Our study compares two primary approaches applied to data generated by ANS: inference-based and direct prediction methods. The selected inference-based approach estimates the health state of the condenser using a pipeline formed by an Auto Associative Kernel Regressor and a Hidden Markov Model (HMM), which subsequently combines the state estimates with its parameters to predict the RUL. The direct prediction method employs a Gradient Boosting Regressor Decision Tree (GBRDT) to map input variables directly to RUL. Our findings demonstrate the efficacy and limitations of each method through the case study, providing valuable insights for the adoption of data-driven RUL estimation techniques in industrial and energy applications.

CycleGAN-based Data Augmentation for Enhanced Remaining Useful Life Prediction Under Unsupervised Domain Adaptation

Predictive maintenance is crucial for enhancing operational efficiency and reducing costs in Prognostics and Health Management (PHM). One of the key tasks in predictive maintenance is the estimation of Remaining Useful Life (RUL) of machinery. In practice, the data for different machines is not always accessible in sufficient quantity or quality, therefore the machine learning models trained on machines in one domain often perform poorly when applied to other domains due to covariate shifts. As a solution, Domain Adaptation (DA) aims to tackle domain shifts by extracting domain-invariant features. However, traditional methods often fail to adequately address the complexity and variability of real-world data. We propose to address this challenge, using a Wasserstein CycleGAN with Gradient Penalty (W-CycleGAN-GP) to learn mappings between domains and generate augmented data in the target domain from data in the source domain. We use our approach to generate realistic augmented data that bridge domain gap coupled with recent work on adversarial-based and correlation alignment-based DA models to improve the performance of RUL prediction models in target domains without having access to labeled data. The experimental results on the C-MAPSS dataset demonstrate a significant improvement in the RUL prediction score and accuracy within the target domain.

Mean Variance Estimation Neural Network Particle Filter for Predicting Battery Remaining Useful Life

Traditional remaining useful life (RUL) prediction methods based on particle filter (PF) require the manual tuning of hyperparameters, such as process or measurement noise, which poses challenges, particularly in real-life applications where external and operating conditions may change, potentially leading to large errors in the predictions. We address this issue by replacing the measurement equation of a PF with a mean variance estimation neural network that estimates the mean and the variance of the output distribution. As a result, the measurement noise is automatically estimated by the neural network and does not require manual setting. Through simulations and comparative analyses with state-of-the-art methods, the proposed mean variance estimation neural network particle filter (MVENN-PF) is shown to provide more stable and accurate RUL predictions, thereby potentially enhancing the robustness of battery health management systems based on it. Additionally, by eliminating the need to manually set a model hyperparameter (the measurement noise) the proposed method simplifies the modeling process, making it more accessible and adaptable to various battery systems.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Universal Remaining Useful Life Prediction for OECTs under Different Aging Conditions

Abstract It is significantly important to predict remaining useful life (RUL) of organic electrochemical transistors (OECTs) for next-generation offshore electronics with stable and reliable performance. Most existing RUL prediction models are not suitable for OECT RUL prediction tasks as they are based on the premise that components have the same aging conditions. In fact, aging conditions of different OECTs often exist discrepancy, leading to performance degradation of RUL prediction models. Although a few methods addressed this issue via transfer learning methods, they still suffer from the challenge in terms of an obvious discrepancy of aging data distribution caused by different aging conditions. To address this issue, we developed a novel universal RUL prediction model for OECTs, called adaptive transformer-based network (ATFN), to reduce the obvious discrepancy among different aging data. First, a transformer-based feature extractor is used to capture the temporal and spatial aging features from some aging precursors. Then, a multi-scale feature alignment metric is adopted to align the aging features of OECTs by reducing discrepancy at different feature scales. Last, an adversarial manner is developed to obtain aging-condition-invariant features for further feature alignment. Extensive experiments are conducted on a real-world OECTs cycling stability aging tests dataset. The average MSE of our method is reduced by two orders of magnitude compared to the one of baseline, which indicates that our method achieves great progress for universal RUL prediction of OECTs under different aging conditions.

Improved machine learning models with a similarity-based approach for remaining useful life prediction

Abstract Cost efficiency is a critical factor in the competitive aviation sector. These efficiency factors force airline operators to develop new approaches in their organizations. Predictive maintenance helps to build scheduling maintenance programs for airline operators or MROs. Scheduled maintenance programs benefit cost efficiency in the aviation sector. Predictive maintenance methods predict the failure time of any equipment. Predictions can be made by analyzing the sensor values from equipment. In this paper, we predicted the remaining useful life (RUL) of turbofan engines using machine learning models and a similarity-based approach. Sensor datasets from the Prognostics Data Repository of NASA, called CMAPPS, were utilized. Using the FD0002 sub-dataset, a health index (HI) was created, and models were trained. Once the models were trained, train and test HIs were estimated. The predicted test HI was matched with the predicted train HI based on a similarity-based approach, and then a RUL prediction was made. The results obtained were compared with the actual results to calculate the accuracy, and the algorithm that resulted in the maximum accuracy was identified. We selected six machine learning algorithms and also created an ensemble model by averaging the predictions of six machine learning algorithms for comparing prediction accuracy. The different algorithms were compared to obtain the prediction model with the closest prediction of remaining useful lifecycle in terms of the number of life cycles. This experiment showed us the effect of the similarity-based approach on the basic version of machine learning models for RUL prediction.