Assoc. Prof. Khanh Nguyen Tarbes National School of Engineering, National Polytechnic Institute of Toulouse, France. Research interests: System reliability, predictive maintenance, machine learning, and prognostic health management.

Title: Artificial Intelligence Enabled Prognostics and Health Management System 
Abstract:
Prognostics and health management (PHM) has been a promising scientific discipline and has become an indispensable component in product life cycles. The PHM procedure consists of seven following tasks: data acquisition, data processing, health indicator construction, fault detection, diagnostic, prognostics, and decision-making. It uses condition monitoring data and the information provided by operational practitioners to assess systems health, detects potential anomalies, diagnoses any incoming faults, and predict the remaining useful life (RUL). The information and indications provided by PHM are then used to schedule maintenance activities, which in turn maintain the system's healthy operation and thus ensure the overall system availability, reliability, and safety. One of the levers to achieve this goal is to replace conventional PHM systems with artificial intelligence (AI) powered solutions. In fact, AI has been demonstrated to increase the automation level not only in data processing and analytics but also in condition monitoring and decision making. In this presentation, we provide a comprehensive view of AI-based methods for the whole PHM process from data acquisition and health indicator construction to decision-making support. We will also give an instructive guideline for researchers and industrial practitioners with varying levels of experience seeking to broaden their skills and knowledge about AI-based PHM implementation.