Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems
This work addresses the need for enhanced robustness in real-world PHM systems for industrial applications, representing an incremental improvement in testing methods.
The paper tackles the problem of evaluating robustness in deep learning-based Prognostics and Health Management systems for Industrial Cyber-Physical Systems by introducing HERO, a black-box adversarial testing framework that generates physically constrained adversarial examples, and it demonstrates the ability to uncover vulnerabilities in state-of-the-art models.
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.