Enhancing failure prediction in nuclear industry: Hybridization of knowledge- and data-driven techniques

TL;DR

This paper introduces a hybrid predictive maintenance approach combining data-driven methods with domain knowledge in the nuclear industry, significantly improving failure prediction accuracy and horizon compared to purely data-driven models.

Contribution

It presents a novel hybrid methodology that integrates domain expertise with data-driven techniques, specifically tailored for the sensitive nuclear sector, enhancing predictive performance.

Findings

Hybrid approach extends prediction horizon to 24 hours

F1 score improves from 56.36% to 93.12%

Outperforms purely data-driven methods in failure prediction

Abstract

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.