2022 journal article

Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 18(10), 7219–7229.

co-author countries: China 🇨🇳 United States of America 🇺🇸
author keywords: Feature extraction; Degradation; Task analysis; Convolutional neural networks; Optimization; Reliability; Training; Adversarial learning; deep transfer learning; infrared thermography; regressive domain adaptation (DA); remaining useful life (RUL) prediction
Source: Web Of Science
Added: September 6, 2022

Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.