Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2758-2763· 0 citations· 24 references
Abstract
Remaining Useful Life (RUL) prediction is critical for predictive maintenance in safety-critical systems such as aerospace engines. While deep learning models achieve high predictive accuracy, they often lack interpretability and reliable uncertainty estimation. This paper proposes the Hybrid Deep Evidential Clustering (HDEC) framework to address both challenges. A CNN–LSTM–GRU backbone first extracts degradation features from multivariate time-series data. These features are then clustered using NN-EVCLUS, an evidential clustering approach based on Dempster–Shafer theory, which groups engines according to their degradation stage. A dedicated RUL predictor is trained for each cluster to enable specialized and interpretable predictions. Engines with uncertain cluster membership are handled through soft memberships, allowing RUL estimation as a weighted combination of cluster-specific predictors instead of hard assignment. Experiments on the NASA C-MAPSS dataset demonstrate that HDEC improves predictive performance while providing well-calibrated uncertainty estimates and interpretable degradation-regime assignments.
Data-driven models are increasingly used to monitor complex industrial systems under varying operating conditions; however, their practical adoption is often constrained by limited labeled data and a lack of interpretability from an end-user perspective, where low-rank representations and context-irrelevant diagnosis are preferred. This study proposes a generative modeling framework for aero-engine fault clustering that emphasizes user-oriented interpretability, bridging data-driven learning with engineering diagnostic practice. The model adds a low-rank discriminative regularization to a mixture-based conditional variational autoencoder, enabling context-free clustering of multiple fault types. First, the learned latent structure yields compact and separable representations for multiple faults. Second, the associated covariance patterns are mapped back to sensor correlations within a reference context, aligning with how maintenance engineers recognize fault symptoms. Experimental results on both N-CMAPSS and real-world aero-engine datasets verify that the proposed approach yields superior clustering performance and reveals interpretable degradation patterns, supporting reliable diagnosis and informed decision-making in complex system health management.
Nan Li, Chen Wang· IEEE Transactions on Reliabi...· 0 citations
The results show that coordinated multi-LLM reasoning can support robust, calibrated, and engineer-interpretable fault classification for automotive HiL validation.
Hamza Ouarrad, Mohammad Abboush, Andreas Rausch· 0 citations
Remaining useful life (RUL) prediction is a critical technology for preventing unexpected failures and reducing maintenance costs in modern industrial systems. However, traditional model‐based approaches are limited by the need for explicit mathematical modeling of degradation mechanisms, while data‐driven methods often require large‐scale datasets and lack interpretability. To address these challenges, this study proposes a Weibull Variational Autoencoder (WVAE). The WVAE is designed to learn probabilistic characteristics grounded in the Weibull distribution from failure history data and predict failure times accordingly. A composite loss function combining mean squared error (MSE) with negative log‐likelihood is employed to jointly ensure predictive accuracy and distributional fidelity, while Monte Carlo Dropout–based inference is used to quantify uncertainty and provide confidence intervals. Simulated datasets incorporating three types of failures, including infant mortality, random, and wear‐out failures, as well as multiple levels of noise, were constructed to reflect diverse system characteristics and industrial conditions. Evaluation results demonstrate that, compared with several benchmarking models, the WVAE produces predictions statistically consistent with historical failure distributions while maintaining stable forecasting performance. Furthermore, by directly estimating the parameters of the Weibull distribution, the WVAE provides statistically interpretable predictions that can be trusted by domain experts. The model is further validated on the Backblaze hard drive field‐reliability dataset, confirming its applicability to real‐world failure data with diverse distributional characteristics.
Unknown authors· Quality and Reliability Engi...· 0 citations
This study proposes a degradation-aware dynamic masking augmentation method combined with a multiscale CNN–Transformer network to address non-stationary monitoring data, limited informative degradation samples, and the difficulty of jointly modeling local degradation patterns and temporal dependencies.
Xudong Song, Guohua Wu, Meng-Dan Wang et al.· Machines· 0 citations
Accurately predicting Remaining Useful Life (RUL) is critical for the intelligent maintenance of high-end rotating machinery, which often operates under complex and variable conditions. However, conventional predictive models tend to degrade in performance when deployed in novel scenarios due to domain shifts. This study proposes a novel predictive framework that integrates Deep Gaussian Processes (DGP) with a dual-dimensional constrained continual learning (CL) strategy to address these challenges. Specifically, the framework leverages the probabilistic nature of DGP to quantify the inherent uncertainty during degradation. To preserve cross-domain degradation knowledge, we employ Elastic Weight Consolidation, which anchors critical weights, and Gradient Episodic Memory, which corrects update directions in the gradient space that conflict with established degradation patterns. Together, these techniques form a dual-dimensional constrained CL framework. This synergistic approach enables the DGP to incrementally assimilate knowledge from different operating conditions (cross-condition) and diverse machine types (cross-device) without suffering from catastrophic forgetting. Experimental results demonstrate that the proposed framework not only exhibits outstanding resistance to catastrophic forgetting and excellent multi-scenario adaptability, but also achieves superior predictive accuracy compared to state-of-the-art methods by leveraging the dual-dimensional continual learning strategy. Furthermore, the integrated DGP module provides robust uncertainty quantification, offering a reliable basis for intelligent maintenance decision-making in complex operating environments.
L. Xiuli, Chen Zifu, Guoxin Wu et al.· Scientific Reports· 0 citations
Remaining Useful Life (RUL) prediction for turbofan engines is critical for balancing operational safety against maintenance costs and environmental impact from premature replacements. Models must reliably extrapolate beyond training data, yet no single method performs optimally across all operational contexts, making model selection fundamentally heuristic. Rather than demonstrating individual model superiority, this work recognizes that reliable prognostic performance emerges from adaptive model contribution. This work introduces an ensemble framework integrating three components: (1) legacy-representative data splitting that mirrors realistic deployment where models predict for newer assets using historical data, (2) median absolute deviation-based outlier filtering for stability, and (3) WTA³ weighting that dynamically adjusts model influence based on cycle-by-cycle performance. This treats model coordination as a time-varying optimization problem adapting to evolving degradation patterns. The framework is validated on NASA CMAPSS data using six diverse models spanning traditional machine learning (Random Forest, XGBoost, Support Vector Regression) and deep learning (LSTM, CNN, Transformer). We investigate how prediction robustness changes during temporal extrapolation, whether adaptive weighting provides more stable forecasts than individual models or fixed combinations, and how uncertainty quantification supports safer maintenance decisions. Initial validation demonstrates that the WTA³ meta-ensemble achieves approximately 3 cycles RMSE and under 3 cycles MAE, representing over 30% improvement over the best individual model, with particularly strong performance in the critical late-life phase. The ensemble maintains highly stable predictions with well-calibrated confidence intervals and substantially improved coverage compared to individual models. This work reframes prognostics from competitive model selection to adaptive coordination, demonstrating that ensemble stability under extrapolation can be systematically achieved. The approach provides actionable confidence bounds for aerospace maintenance programs, enabling cost-efficient scheduling while reducing environmental waste, directly supporting both economic and sustainability objectives where data scarcity and safety criticality are paramount.
Ayushi Bharti, N. Kim, Hee-Cheol Kim· e-Journal of Nondestructive...· 0 citations
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