Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

TL;DR

This paper addresses the challenge of predicting turbofan engine Remaining Useful Life under distribution shifts and limited labeled data by surveying Domain Adaptation (DA) techniques specifically for turbofan RUL. It introduces a novel three-dimensional taxonomy (how, where, why) tailored to turbofan data, and provides a unified view of methodologies from adversarial and metric-based approaches to problem-specific strategies. Benchmarking on C-MAPSS and N-CMAPSS datasets highlights top-performing methods (e.g., CADA, ADARUL, ConsDANN) and offers practical insights on when DA helps versus when it may underperform, alongside complexity considerations. The work also discusses practical deployment issues—privacy, interpretability, and dynamic operating environments—and outlines future directions including privacy-preserving DA, few-shot DA, foundation models, and industry-oriented opportunities, complemented by an open-source collection for reproducibility and further validation.

Abstract

Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating conditions. Domain Adaptation (DA) has emerged as a promising solution, enabling knowledge transfer from source domains with abundant data to target domains with scarce data while mitigating distributional shifts. Given the unique properties of turbofan engines, such as complex operating conditions, high-dimensional sensor data, and slower-changing signals, it is essential to conduct a focused review of DA techniques specifically tailored to turbofan engines. To address this need, this paper provides a comprehensive review of DA solutions for turbofan engine RUL prediction, analyzing key methodologies, challenges, and recent advancements. A novel taxonomy tailored to turbofan engines is introduced, organizing approaches into methodology-based (how DA is applied), alignment-based (where distributional shifts occur due to operational variations), and problem-based (why certain adaptations are needed to address specific challenges). This taxonomy offers a multidimensional view that goes beyond traditional classifications by accounting for the distinctive characteristics of turbofan engine data and the standard process of applying DA techniques to this area. Additionally, we evaluate selected DA techniques on turbofan engine datasets, providing practical insights for practitioners and identifying key challenges. Future research directions are identified to guide the development of more effective DA techniques, advancing the state of RUL prediction for turbofan engines.

Figures (8)

• Figure 1: Overall workflow of DA for RUL prediction. Data is first collected in the source and target domains. After data processing, DA techniques have been adopted to reduce the gaps between domains. After adaptation, the adapted model can then be used in the target domain for RUL prediction.
• Figure 2: Overall summary of DA methods for turbofan engine RUL prediction.
• Figure 3: Workflow of DANN for RUL prediction. The encoder extracts features from both source and target domains, while the domain discriminator tries to distinguish them. A Gradient Reversal Layer (GRL) ensures the encoder learns domain-invariant features by reversing the discriminator’s gradients. Simultaneously, a task-specific linear layer minimizes prediction error on the source domain to improve generalization to the target domain.
• Figure 4: Workflow of ADDA for RUL prediction. A two-stage adversarial framework where the first stage focuses on pretraining the encoder on labeled source data. In the second stage, the pretrained encoder is adapted to the target domain through adversarial learning. The encoder is fine-tuned to produce domain-invariant features, while the domain discriminator is trained to differentiate between the two domains.
• Figure 5: Workflow of metric-based methods for RUL prediction. Metric loss is minimized for reducing domain discrepancies.