Advancing explainable prognostics and health management: Insights from new run-to-failure data of the Tennessee Eastman Process

Abstract

The construction of health indicators (HIs) for failure prognostics is crucial for monitoring and predicting the health state of multi-component systems. Recently, data-driven methods for HI construction and prognostics have gained increasing attention, both relying heavily on high-quality run-to-failure (RTF) data for accurate analysis and reliable prediction. To foster trust in these systems, integrating explainable AI (XAI) into prognostics and health management (PHM), forming XPHM, is highly recommended. However, current literature reveals several research gaps: a lack of RTF data for multicomponent systems, limited consideration of complex component interactions, and insufficient attention to environmental impacts in constructing explainable HIs for prognostics. To address these challenges, this paper introduces new RTF data for the Tennessee Eastman Process (TEP), capturing multicomponent interactions under various environmental conditions through a systematic simulation methodology. Furthermore, the study analyzes this data, elucidating the direct and indirect relationships between sensor measurements and TEP component states across different working conditions. These findings provide valuable insights for researchers and practitioners in developing both component- and system-level HIs and prognostics. Additionally, we illustrate how to use the provided data and analyzed results to construct HIs, further supporting the development of reliable XAI solutions, thereby improving the reliability of engineering systems.