mlsanityData to 7 Oct 2026

Remaining Useful Lifetime Prediction via Deep Domain Adaptation

arXiv 1907.07480 ↗First posted 17 Jul 2019cs.LG stat.MLcited by 30

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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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 6; 2023: 7; 2024: 4; 2025: 4; 2026: 5.

30 papers.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  3. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  4. 2022A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologiescited by 28
  5. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  6. 2021Transferable Time-Series Forecasting under Causal Conditional Shiftcited by 18
  7. 2023Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Predictioncited by 11
  8. 2023A Benchmark on Uncertainty Quantification for Deep Learning Prognosticscited by 9
  9. 2023Context-aware Domain Adaptation for Time Series Anomaly Detectioncited by 8
  10. 2021An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applicationscited by 4
  11. 2022Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variancecited by 4
  12. 2023Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditionscited by 4
18 more
  1. 2024Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictionscited by 4
  2. 2024Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regressioncited by 3
  3. 2021Uncertainty-aware Remaining Useful Life predictorcited by 2
  4. 2021Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networkscited by 2
  5. 2022LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Predictioncited by 2
  6. 2024Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modescited by 2
  7. 2024Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failurescited by 2
  8. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  9. 2025Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trendscited by 1
  10. 2025FTT-GRU: A Hybrid Fast Temporal Transformer with GRU for Remaining Useful Life Predictioncited by 1
  11. 2025Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Predictioncited by 1
  12. 2026Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variabilitycited by 1
  13. 2026Continual Learning of Domain-Invariant Representationscited by 1
  14. 2023A Survey on Deep Industrial Transfer Learning in Fault Prognosticscited by 0
  15. 2023A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensingcited by 0
  16. 2026Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradationcited by 0
  17. 2026Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Predictioncited by 0
  18. 2026CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systemscited by 0