Authors:
Margarida Moreira
1
;
Eliseu Pereira
1
;
2
and
Gil Gonçalves
1
;
2
Affiliations:
1
Faculty of Engineering, University of Porto, Portugal
;
2
SYSTEC-ARISE, Faculty of Engineering, University of Porto, Portugal
Keyword(s):
Predictive Maintenance, Industry 4.0, Remaining Useful Life (RUL), Data-Driven Methods, Survival Analysis.
Abstract:
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 predictive 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 evaluation of survival analysis methods in an industria
l context and develops a user-friendly dashboard for real-time maintenance decision-making. Integrating survival analysis into Industry 4.0 frameworks can significantly enhance predictive maintenance strategies, paving the way for more efficient and reliable industrial operations.
(More)