Explainable AI for post-hoc and pseudo-post-hoc predictive maintenance of governor valve actuators.

ABSTRACT: The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide pr

SUBMITTER: Tang J 

PROVIDER: S-EPMC12606255 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature