HyPredictor: Hybrid Failure Prognosis Approach Combining Data-Driven and Knowledge-Based Methods

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

Authors

Almeida, 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: 245-252

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-GM4

DOI: 10.5220/0012924300003822

Abstract (EN): In modern manufacturing, marked by an unprecedented surge in data generation, utilising this wealth of in formation to enhance company performance has become essential. Within the industrial landscape, one of the significant challenges is equipment failures, which can result in substantial financial losses and wasted time and resources. This work presents the HyPredictor framework, a comprehensive failure prediction and report ing system designed to enhance the reliability and efficiency of industrial operations by leveraging advanced machine learning techniques and domain knowledge. Six machine learning algorithms were evaluated for failure prediction. The predictions from the algorithms are then refined using rule-based adjustments derived from domain knowledge. Additionally, Explainable Artificial Intelligence (XAI) techniques were incorpo rated, as well as the capability of users to customise the system with their own rules and submit failure reports, prompting model retraining and continuous improvement. Integrating domain-specific rules improved the per formance by up to 28 percentage points in the F1 Score metric in some prediction models, with the best hybrid approach achieving an F1 Score of 90% and a Recall of 92% in failure prediction. This adaptive, hybrid ap proach improves prediction accuracy and fosters proactive maintenance, significantly reducing downtime and operational costs. © 2024 by SCITEPRESS¿ Science and Technology Publications, Lda.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7