2020/10/07 by Oscar Serradilla, Serradilla, Oscar, Ekhi Zugasti +3 · 17 citations
Computer Science · Engineering · #A.1 #Anomaly Detection Techniques and Applications #Anomaly detection #Architecture #Artificial intelligence #Business #Computer science #Deep learning #Engineering #FOS: Computer and information sciences #I.5 #Industrial Vision Systems and Defect Detection #J.2 #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #Predictive maintenance #Reliability engineering #Risk analysis (engineering) #Root cause analysis #Systems engineering #Task (project management) #Work (physics) #cs.LG
paper · pdf · doi:10.48550/arxiv.2010.03207
published in arXiv (Cornell University) (Cornell University) · 34 pages, 214 references, 8 tables, 2 equations, 2 figures, survey
arxiv created 2020/10/07 · openalex publication_date 2020/10/07 · arxiv updated 2020/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.