RESEARCH PAPER
Aero-engine remaining useful life prediction: An adaptive method for individual differences of engines
1
Naval Aviation University, China
Submission date: 2025-07-18
Final revision date: 2025-08-13
Acceptance date: 2025-11-11
Online publication date: 2025-12-15
Publication date: 2025-12-15
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(2):214135
HIGHLIGHTS
- The method fully considers the influence of individual differences among engines.
- The proposed model has high prediction accuracy and good generalization.
- The method has higher accuracy and better robustness compared with other methods.
KEYWORDS
TOPICS
ABSTRACT
The remaining useful life (RUL) prediction of aero-engine is of great significance for flight safety. The existing methods do not fully consider the influence of the individual differences of engines on the degradation process. And these methods are difficult to extract key features in highly coupled data, resulting in low prediction accuracy. To solve the problems, an adaptive prediction method for individual differences of engines is proposed. Firstly, the position of the First Prediction Time (FPT) is determined by calculating the maximum volatility value of the Health Indicator (HI) curve, so as to obtain the accurate RUL label. On this basis, the improved Temporal Convolutional Network (TCN) model is used to capture both local degradation patterns and global features. Then enter the bidirectional cyclic network to model the temporal features in the forward and backward directions. Finally, the accurate RUL value is obtained through the deep regression network. Compared with the existing methods, the proposed method has advantages in both prediction accuracy and robustness.
ACKNOWLEDGEMENTS
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors would like to thank the comments from the anonymous reviewers that improved the quality of the paper.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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