mlsanityData to 7 Oct 2026

Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction

arXiv 2302.01704 ↗First posted 3 Feb 2023cs.AIcited by 11

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Abstract (arXiv)

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these methods may not perform well when applied to data from new units of a fleet that follow different operating conditions than those they were trained on. This is also known as domain shifts. Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features. However, DA methods do not distinguish between the different phases of operation, such as steady states or transient phases. This can result in misalignment due to under- or over-representation of different operation phases. This paper proposes two novel DA approaches for RUL prediction based on an adversarial domain adaptation framework that considers the different phases of the operation profiles separately. The proposed methodologies align the marginal distributions of each phase of the operation profile in the source domain with its counterpart in the target domain. The effectiveness of the proposed methods is evaluated using the New Commercial Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan engines operating in one of the three different flight classes (short, medium, and long) are treated as separate domains. The experimental results show that the proposed methods improve the accuracy of RUL predictions compared to current state-of-the-art DA methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 20 of its 67 references that are in the map. First posted on arXiv 3 Feb 2023.

Builds on

20 of 67 references.

References · 20

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  3. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  4. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  5. 2018Taskonomy: Disentangling Task Transfer Learningcited by 631
  6. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
  7. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  8. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  9. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 285
  10. 2021Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 80
  11. 2017AutoDIAL: Automatic DomaIn Alignment Layerscited by 75
  12. 2020Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applicationscited by 63
8 more
  1. 2020Fusing Physics-based and Deep Learning Models for Prognosticscited by 39
  2. 2019Remaining Useful Lifetime Prediction via Deep Domain Adaptationcited by 30
  3. 2020Adversarial Weighting for Domain Adaptation in Regressioncited by 19
  4. 2019Domain Adaptive Transfer Learning for Fault Diagnosiscited by 11
  5. 2021Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosiscited by 10
  6. 2022Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptationcited by 10
  7. 2019Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Modelscited by 5
  8. 2018Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working conditioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 1.

11 papers.

  1. 2024Interpretable Prognostics with Concept Bottleneck Modelscited by 6
  2. 2024Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regressioncited by 3
  3. 2024Health Index Estimation Through Integration of General Knowledge with Unsupervised Learningcited by 3
  4. 2024Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modescited by 2
  5. 2024Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditionscited by 2
  6. 2025Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trendscited by 1
  7. 2025Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Predictioncited by 1
  8. 2023Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarioscited by 0
  9. 2023Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosiscited by 0
  10. 2025Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakerscited by 0
  11. 2026Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstructioncited by 0