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

Miguel Almeida, Eliseu Pereira, Eliseu Pereira, Gil Gonçalves, Gil Gonçalves

2024

Abstract

In modern manufacturing, marked by an unprecedented surge in data generation, utilising this wealth of information 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 reporting 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 incorporated, 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 performance 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 approach improves prediction accuracy and fosters proactive maintenance, significantly reducing downtime and operational costs.

Paper Citation

in Harvard Style

Almeida M., Pereira E. and Gonçalves G. (2024). HyPredictor: Hybrid Failure Prognosis Approach Combining Data-Driven and Knowledge-Based Methods. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 245-252. DOI: 10.5220/0012924300003822

in Bibtex Style

@conference{icinco24,
author={Miguel Almeida and Eliseu Pereira and Gil Gonçalves},
title={HyPredictor: Hybrid Failure Prognosis Approach Combining Data-Driven and Knowledge-Based Methods},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={245-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012924300003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - HyPredictor: Hybrid Failure Prognosis Approach Combining Data-Driven and Knowledge-Based Methods
SN - 978-989-758-717-7
AU - Almeida M.
AU - Pereira E.
AU - Gonçalves G.
PY - 2024
SP - 245
EP - 252
DO - 10.5220/0012924300003822
PB - SciTePress