GMSIT-WDADA: a deep adversarial domain adaptation framework based on state space model for remaining useful life prediction

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is critical for safe, reliable Battery Health Management in diverse operating conditions. Existing RUL models often fail to generalize when test data diverge from the training distribution. To address this, we introduce HybridoNet-Adapt, a domain-adaptive RUL prediction framework that explicitly bridges the gap between labeled source and unlabeled target domains. During training, we minimize the Maximum Mean Discrepancy (MMD) between feature distributions to learn domain-invariant representations. Simultaneously, we employ two parallel predictors—one tailored to the source domain and one to the target domain—and balance their outputs via two learnable trade-off parameters, enabling the model to dynamically weight domain-specific insights. Our architecture couples this adaptation strategy with LSTM, multi-head attention, and Neural ODE blocks for deep temporal feature extraction, but its core novelty lies in the MMD-based alignment and hybrid prediction mechanism. On two large, publicly available battery datasets, HybridoNet-Adapt consistently outperforms non-adaptive baselines (Structural Pruning, Multi-Time Scale Feature Extraction Hybrid model, XGBoost, Elastic Net), archiving an RMSE reduction of up to 152 cycles under domain shifts. These results demonstrate that incorporating domain adaptation into RUL modeling substantially enhances robustness and real-world applicability.

Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.

Remaining useful life (RUL) prediction is crucial for the predictive maintenance and health management of mechanical transmission systems. Although data-driven RUL prediction methods have advanced significantly, three key challenges remain: (i) constructing a physically interpretable HI that is invariant to operating conditions yet sensitive to incipient degradation; (ii) achieving adaptive time to start prediction (TSP) detection that accommodates non-stationarity and controls false and missed alarms; (iii) accurate and generalizable RUL prediction under cross-domain scenarios. To address these issues, a method combining unsupervised anomaly detection and deep feature transfer for TSP determination and RUL prediction is proposed. A residual health indicator is constructed from an expected trajectory learned by Gaussian process regression (GPR) on the healthy segment and a Mahalanobis distance (MD) deviation measure. For the first time extreme value theory is used to create dynamic thresholds for performance degradation analysis and TSP determination. During the RUL prediction phase, transfer component analysis mitigates feature distribution discrepancies between source and target domains. A dual long short-term memory (dual-LSTM) network with L2 regularization is then employed for RUL prediction across two transfer tasks. Experimental validation on a public benchmark dataset and a laboratory run-to-failure bearing dataset demonstrates the superior predictive accuracy and generalization capability of the proposed approach compared to existing methods.

The remaining useful life (RUL) prediction is critical for the safe and reliable operation of lithium-ion battery (LIB) systems, which characterizes the aging status of the battery and provides early warning for battery replacement. Most existing RUL prediction methods rely on empirical aging models, and the role of the battery mechanism is not considered in the subsequent algorithm settings. The accuracy and stability of data-driven algorithms are severely limited by battery aging data. A new electrochemical-model-based particle filter (PF) framework for LIB RUL prediction is proposed in this paper. Parameters of a simplified electrochemical model (SEM) are used as state variables of the PF algorithm and these parameters can be identified by applying specially designed current excitations to the battery. The SEM-based capacity simulation process is taken as the observation equation in the PF algorithm framework. Therefore, the mechanism of the battery is fully considered when making the RUL prediction. The proposed method is validated through cyclic aging experiment of a cylindrical LFP/graphite LIB of 45Ah. The accuracy of the method is compared with a data-driven-based PF framework for RUL prediction and shows better accuracy and stability, which provides a choice for achieving high-quality RUL prediction.

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between non-stationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). Firstly, the multi-direction features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multi-direction information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning.

Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM), playing a crucial role in ensuring the reliability and safety of equipment. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, many current deep learning models still fall short in comprehensive feature extraction. To tackle this limitation, we propose a multi-feature fusion model leveraging a dual-attention mechanism. Initially, convolutional neural networks (CNN) and channel attention mechanisms are employed to preliminarily extract spatial features. Subsequently, a layer combining Long Short-Term Memory (LSTM) and self-attention mechanisms is used to further extract and integrate temporal features. Concurrently, domain knowledge-based handcrafted features are selected for multi-feature fusion. Finally, RUL values are predicted via regression. The effectiveness of the proposed method is validated on widely-used academic datasets, and its superior performance in RUL prediction is demonstrated through comparative analysis with other methods.

Tool condition monitoring can be employed to ensure safe and full utilization of the cutting tool. Hence, remaining useful life (RUL) prediction of a cutting tool is an important issue for an effective high-speed milling process-monitoring system. However, it is difficult to establish a mechanism model for the life decreasing process owing to the different wear rates in various stages of cutting tool. This study proposes a three-stage Wiener-process-based degradation model for the cutting tool wear estimation and remaining useful life prediction. Tool wear stages classification and RUL prediction are jointly addressed in this work in order to take full advantage of Wiener process, as this three-stage Wiener process definitely constitutes to describe the degradation processes at different wear stages, based on which the overall useful life can be accurately obtained. The numerical results obtained using extensive experiment indicate that the proposed model can effectively predict the cutting tool’s remaining useful life. Empirical comparisons show that the proposed model performs better than existing models in predicting the cutting tool RUL.

Organic electrochemical transistors (OECTs) show abundant potential in biosensors, artificial neuromorphic systems, brain-machine interfaces, etc With the fast development of novel functional materials and new device structures, OECTs with high transconductance (gm > mS) and good cycling stabilities (> 10,000 cycles) have been developed. While stability characterization is always time-consuming, to accelerate the development and commercialization of OECTs, tools for stability prediction are urgently needed. In this paper, OECTs with good cycling stabilities are realized by minimizing the gate voltage amplitude during cycling, while a remaining useful life (RUL) prediction framework for OECTs is proposed. Specifically, OECTs based on p(g2T-T) show tremendously enhanced stability which exhibits only 46.1% on-current (ION) and 33.2% peak gm decreases after 80,000 cycles (53 min). Then, RUL prediction is proposed based on the run-to-failure (RtF) aging tests (cycling stability test of OECTs). By selecting two aging parameters (ION and peak gm) as health indicators (HI), a novel multi-scale feature fusion (MFF) method for RUL prediction is proposed, which consists of a long short-term memory (LSTM) neural network based multi-scale feature generator (MFG) module for feature extraction and an attention-based feature fusion (AFF) module for feature fusion. Consequently, richer effective information is utilized to improve the prediction performance, where the experimental results show the superiority of the proposed framework on multiple OECTs in RUL prediction tasks. Therefore, by introducing such a powerful framework for the evaluation of the lifetime of OECTs, further optimization of materials, devices, and integrated systems relevant to OECTs will be stimulated. Moreover, this tool can also be extended to other relevant bioelectronics.

In Industry 4.0, the integration of artificial intelligence (AI) has transformed industrial operations, with predictive maintenance (PdM) emerging as a key application. As the core of PdM, remaining useful life (RUL) prediction enables a shift from reactive or preventive maintenance to proactive, data-driven strategies, helping to optimize scheduling, reduce downtime, and cut costs. Despite the high predictive accuracy of machine learning and deep learning models in RUL prediction, their inherent ‘black-box’ nature presents significant barriers to practical industrial adoption. These barriers include compromised trust in model outputs, difficulties in debugging and error tracing, challenges in meeting regulatory compliance requirements, and limited alignment with domain-specific expertise. Consequently, explainable AI (XAI) has become indispensable for addressing these issues and enabling wider deployment of RUL prediction systems in industrial settings. This paper presents a comprehensive survey of XAI for industrial RUL prediction. The survey begins by exploring the significance of RUL prediction, challenges of black-box models, the role of XAI, and its value in enhancing the reliability of RUL methods. Subsequently, it systematically synthesizes recent research advancements, categorizes XAI techniques into five types, and traces their technical evolution. It also develops a holistic evaluation framework encompassing approaches, properties, and metrics, supported by 11 functionally grounded quantitative indicators. Moreover, it establishes a comparative analysis system on eight typical public datasets to clarify their suitability for XAI integration and industrial applications, and puts forward future research directions that combine data, physics, large language models, and digital twins to bridge the gap between academic research and industrial deployment.

PSTFormer: A novel parallel spatial-temporal transformer for remaining useful life prediction of aeroengine

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.

Bearings are critical components in mechanical systems, and their degradation process typically exhibits distinct stages, making stage-based remaining useful life (RUL) prediction highly valuable. This paper presents a model that combines correlation analysis feature extraction with a Graph Neural Network (GNN)-based approach for bearing degradation stage classification and RUL prediction, aiming to achieve accurate bearing life prediction. First, the proposed Pearson–Spearman correlation metric, along with Kernel Principal Component Analysis (KPCA) and autoencoders, is used to group and fuse health indicators (HIs), thereby obtaining a health indicator (HI) that effectively reflects the bearing degradation process. Then, a model combining Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) networks is proposed for bearing degradation stage classification. Based on the classification results, the Adaptive Attention GraphSAGE–LSTM (AAGL) model, also introduced in this study, is employed to precisely predict the bearing’s remaining useful life.

Predicting accurate remaining useful life (RUL) is quite necessary in many industrial cases. Recently, data-driven method has been widely used for RUL prediction because of its promising performance and ease of use. However, one practical challenging issue of the data-driven method is lack of labeled data. One way for dealing with this issue is domain adaptation. With domain adaptation technique, a RUL prediction model is built using labeled data of source domain and then the trained model is used to predict RUL of target domain where labeled data is unavailable. In this paper, we propose our method for RUL prediction through domain adaptation. Another practical issue of RUL prediction is that a single RUL prediction value may not be enough for successful maintenance of target systems. In other words, we need to know how uncertain RUL prediction is. In this paper, we study Monte Carlo (MC) dropout-based uncertainty metric for realizing better RUL prediction result. Through experiment, we first show that our domain adaptation technique shows reasonable performance. We also show that MC dropout-based approach generates the uncertainty metric only meaningful for source domain.

Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance of rotating machinery. However, the effectiveness of many existing RUL prediction methods depends on expert experience and signal processing algorithms, which limiting the application of these methods in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting of a multi-scale attention-based dilated causal convolutional (MADCC) module and a multi-layer temporal convolutional network (MTCN) to predict the RUL of bearings using raw vibration data. First, the MADCC module extracts multi-scale temporal features of the bearing degradation process (BDP) and provides fused feature vectors (FFVs) containing comprehensive BDP information for the MTCN module. Subsequently, the MTCN module mines deep temporal dependencies hidden in the FFV to predict the RUL of bearings. Ablation experiments are conducted to analyze the contribution of the framework’s components. Three evaluation metrics (root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2)) are used to verify the effectiveness of the proposed framework and other state-of-the-art methods on two public bearing datasets. The experimental results show that the proposed framework achieves the lowest RMSE and MAE and the highest R 2, demonstrating excellent performance and potential for RUL prediction of bearings.

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