Aug 2026· e-Journal of Nondestructive Testing· Vol 31· 0 citations
Abstract
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.
Ensuring reliability, safety, and economic efficiency in airline operations requires maintenance and fleet scheduling strategies that explicitly account for uncertainty in Remaining Useful Life (RUL) predictions. However, the integration of prognostic uncertainty into operational decision-making remains a major challenge. In practice, tail assignment (TA) and maintenance scheduling (MS) are typically optimized separately or sequentially, thereby limiting the effective use of predictive health information despite their strong interdependencies. This paper proposes a unified optimisation framework that jointly integrates TA, MS, and predictive maintenance (PdM) under RUL with confidence intervals. The problem is formulated as a stochastic mixed-integer linear program, and a scalable solution approach is developed by embedding a neural network surrogate to approximate expected disruption costs resulting from RUL uncertainty. The proposed framework is evaluated using operational scenarios derived from real-world airline data. Results show that explicitly incorporating prognostic uncertainty in a joint planning model reduces operational risk, i.e., downstream disruption costs and flight cancellations, compared to deterministic and sequential approaches, at the expense of moderate increases in planning cost. These findings highlight the value of tightly coupling predictive maintenance with operational planning and demonstrate the potential of surrogate-assisted stochastic optimisation for scalable, uncertainty-aware airline decision-making.
Benno Käslin, Marta Ribeiro, D. Zarouchas et al.· 0 citations
This work presents a protocol-aware empirical assessment across three settings: a C-MAPSS degradation-risk proxy, normal-only training for anomalous-sound detection on MIMII, and BDG2 forecasting-residual diagnostics with synthetic target perturbations.
The purpose of this study is to develop and validate a robust framework for data-driven predictive maintenance (PdM) that estimates the remaining useful life (RUL) of equipment and signals the optimal time to initiate maintenance activities. By integrating statistical modeling and machine learning techniques, the proposed framework aims to minimize unplanned downtimes, reduce maintenance costs and enhance operational efficiency. It addresses critical gaps in existing methods by providing real-time condition monitoring and predictive alerts, enabling maintenance personnel to make informed decisions and optimize maintenance scheduling.
This study introduces a data-driven predictive maintenance framework designed to estimate the RUL of equipment and provide timely signals for initiating maintenance tasks. The framework utilizes a combination of statistical modeling and machine learning algorithms. A Weibull distribution with time-varying parameters is employed to model the RUL, while random forest and exponentially weighted moving average (EWMA) control charts are integrated for failure prediction and maintenance signaling. The methodology is validated using a synthetic dataset that simulates real-world scenarios, enabling robust evaluation of the proposed approach in terms of accuracy and reliability.
The findings demonstrate that the proposed predictive maintenance framework effectively estimates the RUL of equipment with high accuracy and provides timely signals for initiating maintenance tasks. Validation on a synthetic dataset reveals that the framework consistently predicts failure probabilities and generates alerts well in advance, allowing sufficient time for maintenance planning. The integration of Weibull distribution modeling and Random Forest classifiers enhances the reliability of predictions, while the use of EWMA control charts ensures robust monitoring of failure probabilities. These results highlight the framework's potential to reduce downtimes and maintenance costs.
This study introduces a novel data-driven predictive maintenance framework for accurately estimating the RUL of equipment and providing timely signals for maintenance actions. Unlike existing methods, the proposed approach integrates a probabilistic model with machine learning techniques, leveraging time-varying Weibull distributions and advanced statistical tools for robust predictions. The framework not only improves failure prediction accuracy but also enhances maintenance planning by signaling appropriate times for action. This contributes to reducing downtime and costs while increasing operational efficiency, offering a significant advancement for Industry 5.0 and smart manufacturing applications.
Ahmad Razavi, M. R. Rasouli, M. Pishvaee et al.· Journal of Quality in Mainte...· 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
Timely asset maintenance remains a critical challenge in Industry 4.0 environments. Predictive Maintenance aims to anticipate failures and estimate Remaining Useful Life (RUL), enabling cost reduction and minimizing production downtime. However, real-world industrial scenarios are often characterized by noisy telemetry data, incomplete information about operating conditions, and weak degradation signals, which limit the effectiveness of conventional data-driven approaches. This paper proposes a methodology for RUL prediction under such challenging conditions, leveraging raw sensor telemetry without requiring detailed knowledge of machine operating characteristics. The approach introduces a novel Degradation Index, combined with a Health Index, to better represent degradation patterns. Additionally, signal preprocessing techniques, including Savitzky–Golay and Kalman filters, are applied to mitigate noise and improve data quality. The methodology integrates statistical analysis, similarity-based pattern extraction, and machine learning techniques, including Convolutional Neural Networks and Long Short-Term Memory models, for feature selection and prediction. Experiments conducted on real-world industrial datasets demonstrate that the proposed approach significantly improves prediction performance, achieving high accuracy and enabling failure anticipation up to five days in advance. The results highlight the importance of feature engineering and signal processing in PdM applications, showing that combining degradation modeling with deep learning yields robust, generalizable RUL predictions, even in noisy, partially observed environments.
Tiago Zonta, C. D. da Costa, F. Zeiser et al.· Scientific Reports· 0 citations
: With the deepening integration of renewable energy and smart grid technologies, the accurate prediction of power equipment failures and assessment of their health status have become increasingly critical. Current methodologies often operate in isolation: generalized linear models excel in static health assessment but fail to capture temporal dynamics, while time series models forecast trends but lack mechanistic insight into failures. This paper bridges this gap by proposing a comprehensive hybrid model that fuses the interpretability of GLMs with the predictive power of time series analysis. Our approach begins with rigorous data cleaning and preprocessing of historical sensor data. We then implement a two-stage strategy: Stage 1 employs a Poisson regression model to estimate a foundational health index, translating static operational conditions into an interpretable score. In Stage 2, this health score sequence is treated as a new time series and modeled jointly by an ARIMA component for linear trend analysis and an LSTM network for learning non-linear patterns, resulting in a robust fault trend prediction. Experimental results on real operational datasets confirm that our model achieves superior performance compared to all baseline models, showing significant reductions in RMSE and improvements in F1-score and AUC. The framework effectively provides earlier warnings of sub-healthy states, offering a practical and innovative solution for intelligent, data-driven maintenance strategies in the power industry.
Bowen Yu, Jialin He· Proceedings of the 1st Inter...· 0 citations
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