A Systematic Survey of Unsupervised Deep Learning Techniques for Structural Health Monitoring and Predictive Maintenance
Abstract
This paper presents a systematic survey of unsupervised deep learning techniques applied tostructural health monitoring (SHM) and predictive maintenance (PdM). The survey reviewsautoencoders, clustering-based approaches, anomaly detection models, commonly used datasets,evaluation metrics, challenges, and future research directions.
Fields of Study & Subjects
Structural Health Monitoring Techniques
Machine Fault Diagnosis Techniques
Infrastructure Maintenance and Monitoring
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 7 February 2026
- Language
- EN
- Document type
- Preprint