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Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems

Hu, Jinwei; Tang, Zezhi; Jin, Xin; Zhang, Benyuan; Dong, Yi; Huang, Xiaowei; (2025) Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems. IEEE Transactions on Industrial Cyber-Physical Systems , 3 pp. 472-484. 10.1109/ticps.2025.3586988. Green open access

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Abstract

This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO’s ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.

Type: Article
Title: Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ticps.2025.3586988
Publisher version: https://doi.org/10.1109/ticps.2025.3586988
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Prognostics and health management, Testing, Robustness, Data models, Predictive models, Transformers, Adaptation models, Accuracy, Rabbits, Optimization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10211597
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