# Remaining Useful Lifetime Prediction via Deep Domain Adaptation

arXiv: https://arxiv.org/abs/1907.07480 · HTML: https://mlsanity.com/paper/1907.07480/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 30 of 30)

- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift](https://mlsanity.com/paper/2204.03342/) (2022)
- [Transferable Time-Series Forecasting under Causal Conditional Shift](https://mlsanity.com/paper/2111.03422/) (2021)
- [Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction](https://mlsanity.com/paper/2302.01704/) (2023)
- [A Benchmark on Uncertainty Quantification for Deep Learning Prognostics](https://mlsanity.com/paper/2302.04730/) (2023)
- [Context-aware Domain Adaptation for Time Series Anomaly Detection](https://mlsanity.com/paper/2304.07453/) (2023)
- [An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications](https://mlsanity.com/paper/2111.00874/) (2021)
- [Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance](https://mlsanity.com/paper/2205.03554/) (2022)
- [Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions](https://mlsanity.com/paper/2304.11702/) (2023)
- [Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions](https://mlsanity.com/paper/2404.04824/) (2024)
- [Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression](https://mlsanity.com/paper/2401.13721/) (2024)
- [Uncertainty-aware Remaining Useful Life predictor](https://mlsanity.com/paper/2104.03613/) (2021)
- [Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks](https://mlsanity.com/paper/2109.12513/) (2021)
- [LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction](https://mlsanity.com/paper/2208.08388/) (2022)
- [Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes](https://mlsanity.com/paper/2405.06068/) (2024)
- [Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures](https://mlsanity.com/paper/2410.22594/) (2024)
- [Time Series Domain Adaptation via Latent Invariant Causal Mechanism](https://mlsanity.com/paper/2502.16637/) (2025)
- [Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2510.03604/) (2025)
- [FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Prediction](https://mlsanity.com/paper/2511.00564/) (2025)
- [Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction](https://mlsanity.com/paper/2512.02610/) (2025)
- [Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability](https://mlsanity.com/paper/2603.24475/) (2026)
- [Continual Learning of Domain-Invariant Representations](https://mlsanity.com/paper/2605.15775/) (2026)
- [A Survey on Deep Industrial Transfer Learning in Fault Prognostics](https://mlsanity.com/paper/2301.01705/) (2023)
- [A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing](https://mlsanity.com/paper/2306.04919/) (2023)
- [Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation](https://mlsanity.com/paper/2603.15687/) (2026)
- [Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction](https://mlsanity.com/paper/2609.25179/) (2026)
- [CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems](https://mlsanity.com/paper/2609.39784/) (2026)

## Abstract (arXiv)

In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions. However, due to different operating conditions, fault modes, noise and equipment updates distribution shift exists across different data domains. This shift reduces the performance of predictive models previously built to specific conditions when no observed run-to-failure data is available for retraining. To address this issue, this paper proposes a new data-driven approach for domain adaptation in prognostics using Long Short-Term Neural Networks (LSTM). We use a time window approach to extract temporal information from time-series data in a source domain with observed RUL values and a target domain containing only sensor information. We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain. The experimental results show that the proposed method can provide more reliable RUL predictions under datasets with different operating conditions and fault modes. These results suggest that the proposed method offers a promising approach to performing domain adaptation in practical PHM applications.