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Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation

arXiv:2603.15687v1 Announce Type: new Abstract: Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by transferring knowledge from a labeled source domain to an unlabeled target domain. Despite its success, existing DA methods struggle significantly when faced with incomplete degradation trajectories in the target domain, particularly due to the absence of late degradation stages. This missing data introduces a key extrapolation challenge. When applied to such incomplete RUL prediction tasks, current DA methods encounter two primary limitations. First, most DA approaches primarily focus on global alignment, which can misaligns late degradation stage in the source domain with early degradation stage in the target domain. Second, due to varying operating conditions in RUL prediction, degradation patterns may differ even within the same degradation stage, resulting in

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Yubo Hou, Mohamed Ragab, Yucheng Wang, Min Wu, Abdulla Alseiari, Chee-Keong Kwoh, Xiaoli Li, Zhenghua Chen · · 1 min read · 6 views
#cs.LG #cs.AI

arXiv:2603.15687v1 Announce Type: new Abstract: Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by transferring knowledge from a labeled source domain to an unlabeled target domain. Despite its success, existing DA methods struggle significantly when faced with incomplete degradation trajectories in the target domain, particularly due to the absence of late degradation stages. This missing data introduces a key extrapolation challenge. When applied to such incomplete RUL prediction tasks, current DA methods encounter two primary limitations. First, most DA approaches primarily focus on global alignment, which can misaligns late degradation stage in the source domain with early degradation stage in the target domain. Second, due to varying operating conditions in RUL prediction, degradation patterns may differ even within the same degradation stage, resulting in different learned features. As a result, even if degradation stages are partially aligned, simple feature matching cannot fully align two domains. To overcome these limitations, we propose a novel evidential adaptation approach called EviAdapt, which leverages evidential learning to enhance domain adaptation. The method first segments the source and target domain data into distinct degradation stages based on degradation rate, enabling stage-wise alignment that ensures samples from corresponding stages are accurately matched. To address the second limitation, we introduce an evidential uncertainty alignment technique that estimates uncertainty using evidential learning and aligns the uncertainty across matched stages.

Executive Summary

The article proposes a novel evidential adaptation approach, EviAdapt, to enhance domain adaptation for Remaining Useful Life (RUL) prediction with incomplete degradation trajectories. EviAdapt addresses the limitations of existing domain adaptation methods by segmenting data into distinct degradation stages and introducing an evidential uncertainty alignment technique. This approach ensures accurate matching of samples from corresponding stages and aligns uncertainty across matched stages, leading to improved RUL prediction.

Key Points

  • ▸ EviAdapt proposes a stage-wise alignment approach to match samples from corresponding degradation stages
  • ▸ The method introduces an evidential uncertainty alignment technique to estimate and align uncertainty across matched stages
  • ▸ EviAdapt addresses the limitations of existing domain adaptation methods in handling incomplete degradation trajectories

Merits

Improved Accuracy

EviAdapt's stage-wise alignment and evidential uncertainty alignment techniques can lead to more accurate RUL predictions

Robustness to Incomplete Data

The approach can handle incomplete degradation trajectories, making it more robust than existing methods

Demerits

Computational Complexity

The introduction of evidential learning and uncertainty alignment techniques may increase computational complexity

Limited Generalizability

The approach may not generalize well to other domains or applications without significant modifications

Expert Commentary

The proposed EviAdapt approach addresses a critical challenge in RUL prediction, namely the handling of incomplete degradation trajectories. By introducing a stage-wise alignment approach and evidential uncertainty alignment technique, EviAdapt can improve the accuracy and robustness of RUL predictions. However, further research is needed to evaluate the approach's computational complexity, generalizability, and applicability to various domains. The implications of EviAdapt are significant, as it can inform maintenance scheduling, resource allocation, and risk management decisions in industries that rely on RUL prediction.

Recommendations

  • ✓ Further evaluation of EviAdapt's computational complexity and generalizability to other domains and applications
  • ✓ Investigation of the approach's potential applications in other fields, such as healthcare or finance, where RUL prediction is crucial

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