Data-Driven Predictive Maintenance for Component Life-Cycle Extension

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Moreira, M (Author) Other; Pereira, E (Author) FEUP; Gil Manuel Gonçalves (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Conference on Informatics in Control, Automation and Robotics

Pages: 126-136

21st International Conference on Informatics in Control, Automation and Robotics, ICINCO 2024

Porto, 18 November 2024 through 20 November 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-018-GJH

DOI: 10.5220/0013014200003822

Abstract (EN): In the era of Industry 4.0, predictive maintenance is crucial for optimizing operational efficiency and reducing downtime. Traditional maintenance strategies often cost more and are less reliable, making advanced pre dictive models appealing. This paper assesses the effectiveness of different survival analysis models, such as Cox Proportional Hazards, Random Survival Forests (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machines (FS-SVM), in predicting equipment failures. The models were tested on datasets from Gorenje and Microsoft Azure, achieving C-index values on test data such as 0.792 on the Cox Model, 0.601 using RSF, 0.579 using the GBSA model and 0.514 when using the FS-SVM model. These results demonstrate the models¿ potential for accurate failure prediction, with FS-SVM showing significant improvement in test data compared to its training performance. This study provides a comprehensive evalua tion of survival analysis methods in an industrial context and develops a user-friendly dashboard for real-time maintenance decision-making. Integrating survival analysis into Industry 4.0 frameworks can significantly en hance predictive maintenance strategies, paving the way for more efficient and reliable industrial operations. © 2024 by SCITEPRESS¿ Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10