A two-layer stacked LSTM architecture is integrated with multi-head temporal attention (eight heads) to capture short- and long-term degradation patterns, and a feature attention module to dynamically weight sensor channels. An asymmetric loss function penalizes overestimation, and attention regularization promotes head diversity. The model is trained and validated on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) data set using RMSE, R1 and custom metrics.
This study aims to enhance the accuracy and reliability of Remaining Useful Life (RUL) prediction for aircraft engines in Prognostics and Health Management (PHM) systems. By addressing limitations in standard LSTM models, such as capturing long-range dependencies and handling noisy sensor data, the research proposes a modified LSTM framework tailored for multivariate time-series data from IoT-enabled engines in Web-based environments.
Experiments on C-MAPSS subsets demonstrate superior performance: achieving an RMSE of 17.80 compared to 28.21 for the baseline LSTM. Multi-head attention (eight heads) outperforms variants (2 / 4/16 heads), balancing complexity and accuracy, especially under asymmetric penalties.
Limited to C-MAPSS data set; real-world deployment may require adaptation to diverse fault modes. Implications include advancing attention-enhanced models for non-stationary signals, with future extensions to federated learning and domain-specific integrations for broader PHM applications.
This study provides significant practical contributions to the field of PHM within Industrial IoT (IIoT) frameworks. By achieving high-precision RUL predictions (with RMSE reduced to the 19.90–28.93 range), the proposed model offers a robust technical foundation for transitioning from traditional scheduled maintenance to cost-effective predictive maintenance, maximizing component utility while minimizing unscheduled downtime.
Enhances flight safety by enabling proactive interventions, minimizing unexpected failures. Promotes sustainable aviation through extended component lifespans, reducing environmental impact from frequent replacements and supporting global safety standards.
Novel hybrid LSTM with hierarchical multi-head temporal and feature attention, plus asymmetric loss and regularization, outperforms state-of-the-art methods in accuracy (up to 60\% RMSE reduction) and reliability.
