Open AccessOptimizing state monitoring with domain degradation knowledge
Author(s) -
Zhevnenko Dmitry,
Makarov Ilya
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3573683
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Monitoring the degradation of industrial equipment is vital for maintaining efficient production. Existing deep learning-based monitoring techniques often focus on isolated target characteristics, failing to capture the device’s condition throughout its degradation process comprehensively. We propose a novel approach to constructing a unified feature space incorporating degradation-based features from multiple degradation characteristics. The proposed method enhances the predictive accuracy of degradation parameters, improves model interpretability, and minimizes overfitting by avoiding reliance on unrealistic characteristic patterns. We introduce a new model architecture, Industrial Health Index Extraction, designed to implement this approach effectively. Our methodology demonstrates state-of-the-art performance on self-supervised and supervised tasks using NASA’s CMAPSS and milling datasets.