Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder

2026-03-11

AI Summary

This work addresses the challenge of distribution mismatch between source and target domains in health indicator modeling under varying operating conditions, particularly caused by misaligned degradation stages and the limited ability of 1D-CNNs to capture long-range dependencies. To tackle this, the authors propose a novel domain adaptation framework that introduces a degradation-stage-synchronized batch sampling strategy to align domains across different operational conditions. Furthermore, they design a cross-domain autoencoder integrating large convolutional kernels with a cross-attention mechanism to effectively learn domain-invariant health representations. Experimental results on the Korean Defense System and XJTU-SY bearing datasets demonstrate that the proposed method outperforms state-of-the-art approaches by an average of 24.1%, significantly enhancing the accuracy and robustness of health indicator construction across diverse operating conditions.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The construction of high quality health indicators (HIs) is crucial for effective prognostics and health management. Although deep learning has significantly advanced HI modeling, existing approaches often struggle with distribution mismatches resulting from varying operating conditions. Although domain adaptation is typically employed to mitigate these shifts, two critical challenges remain: (1) the misalignment of degradation stages during random mini-batch sampling, resulting in misleading discrepancy losses, and (2) the structural limitations of small-kernel 1D-CNNs in capturing long-range temporal dependencies within complex vibration signals. To address these issues, we propose a domain-adaptive framework comprising degradation stage synchronized batch sampling (DSSBS) and the cross-domain aligned fusion large autoencoder (CAFLAE). DSSBS utilizes kernel change-point detection to segment degradation stages, ensuring that source and target mini-batches are synchronized by their failure phases during alignment. Complementing this, CAFLAE integrates large-kernel temporal feature extraction with cross-attention mechanisms to learn superior domain-invariant representations. The proposed framework was rigorously validated on a Korean defense system dataset and the XJTU-SY bearing dataset, achieving an average performance enhancement of 24.1% over state-of-the-art methods. These results demonstrate that DSSBS improves cross-domain alignment through stage-consistent sampling, whereas CAFLAE offers a high-performance backbone for long-term industrial condition monitoring.

Problem

• domain adaptation
• health indicator
• degradation stage alignment
• long-range temporal dependencies
• distribution mismatch

Innovation

• domain adaptation
• degradation-stage synchronization
• large-kernel CNN
• cross-attention
• health indicator