Health index estimation through integration of general knowledge with unsupervised learning

• Published in: Reliability engineering & system safety Vol. 251; p. 110352
• Main Authors: Bajarunas, Kristupas, Baptista, Marcia L., Goebel, Kai, Chao, Manuel Arias
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Convolutional autoencoder; Drift och underhållsteknik; Health index; Hybrid model; Operation and Maintenance Engineering; Prognostics; Unsupervised learning; Hybrid model; Health index; Convolutional autoencoder; Prognostics; Unsupervised learning
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2024.110352

Accurately estimating a Health Index (HI) from condition monitoring data (CM) is essential for reliable and interpretable prognostics and health management (PHM) in complex systems. In most scenarios, complex systems operate under varying operating conditions and can exhibit different fault modes, making unsupervised inference of an HI from CM data a significant challenge. Hybrid models combining prior knowledge about degradation with deep learning models have been proposed to overcome this challenge. However, previously suggested hybrid models for HI estimation usually rely heavily on system-specific information, limiting their transferability to other systems. In this work, we propose an unsupervised hybrid method for HI estimation that integrates general knowledge about degradation into the convolutional autoencoder’s model architecture and learning algorithm, enhancing its applicability across various systems. The effectiveness of the proposed method is demonstrated in two case studies from different domains: turbofan engines and lithium batteries. The results show that the proposed method outperforms other competitive alternatives, including residual-based methods, in terms of HI quality and their utility for Remaining Useful Life (RUL) predictions. The case studies also highlight the comparable performance of our proposed method with a supervised model trained with HI labels. [Display omitted] •Unsupervised hybrid method for health index estimation of complex systems.•The method combines multiple hybridization strategies with low-fidelity knowledge.•Benefit from general knowledge: generalizability across multiple systems.•Method evaluated on the N-CMAPSS aero-engine and NASA battery usage case studies.•The proposed method outperforms the industry-standard residual method.