Ph. D. Project
Title:
Event-based predictive analytics for failure prediction of industrial systems
Dates:
2025/03/17 - 2028/03/16
Student:
Supervisor(s):
Description:
General context
The future PhD student will be recruited at CRAN laboratory to develop his PhD in the framework of the project ANR-FACEPE TECHMAINT (Event-based predictive analytics for health management of industrial systems). The TECHMAINT project seeks to develop predictive analytics models based on discrete event data for managing the system health and states.
Scientific context and goals:
With the fast development of information and sensing technology as advocated by Industry 4.0 initiative, Prognostics and Health Management (PHM) is now widely investigated in industrial systems to contribute to their reliability, supportability, and economic affordability. Indeed, PHM is more an integrated approach for managing the system health in a life cycle perspective with a focus on failure prediction and maintenance optimization. Many prognostics models, especially data-driven ones, have been proposed and successfully applied to various industrial applications thanks to the advancement of predictive analytics techniques (predictive modelling, Machine Learning). The data can be categorized into condition monitoring data (CM) referred to the data measured by sensors in real-time and event data. Although CM data-based prognostic approaches are the most studied, continuous monitoring on an industrial plant is usually expensive or even impossible for certain complex systems despite its prevalence and effectiveness. One alternative for the prognostics purpose is therefore, to effectively incorporate the analysis of (discrete) event data. Event data provides useful information on what happened and what was done to the system. They are via processing supported by PLCs, supervision system, SCADA, etc. or via inspection, and can be considered in most of the cases as information or an indicator because already processed to be semantically consistent. So, this data provides rich information of system operation (without adding sensors even if some events are built from CM) and its degradation process.
The aim of this thesis is to develop advanced prognostics approaches using (discrete) event data for predicting the failure or remaining useful life (RUL) of industrial systems. This PhD program is structured in three major phases: (1)-characterization of relevant events related to different types of items (i.e. component, machine, system) to have the necessary indicators that make sense in terms of a reliable prognostics on this item; (2)- pattern construction and recognition that should allow isolating patterns representative of nominal operation, and those representing item failure progression; and (3)-development of advanced prognostics approaches to predict the RUL of an item from occurrences of patterns that are linked with the failure or degradation of the item. The proposed approaches/models will be tested and validated by experimental dataset or/and real-industrial use cases.
The future PhD student will be recruited at CRAN laboratory to develop his PhD in the framework of the project ANR-FACEPE TECHMAINT (Event-based predictive analytics for health management of industrial systems). The TECHMAINT project seeks to develop predictive analytics models based on discrete event data for managing the system health and states.
Scientific context and goals:
With the fast development of information and sensing technology as advocated by Industry 4.0 initiative, Prognostics and Health Management (PHM) is now widely investigated in industrial systems to contribute to their reliability, supportability, and economic affordability. Indeed, PHM is more an integrated approach for managing the system health in a life cycle perspective with a focus on failure prediction and maintenance optimization. Many prognostics models, especially data-driven ones, have been proposed and successfully applied to various industrial applications thanks to the advancement of predictive analytics techniques (predictive modelling, Machine Learning). The data can be categorized into condition monitoring data (CM) referred to the data measured by sensors in real-time and event data. Although CM data-based prognostic approaches are the most studied, continuous monitoring on an industrial plant is usually expensive or even impossible for certain complex systems despite its prevalence and effectiveness. One alternative for the prognostics purpose is therefore, to effectively incorporate the analysis of (discrete) event data. Event data provides useful information on what happened and what was done to the system. They are via processing supported by PLCs, supervision system, SCADA, etc. or via inspection, and can be considered in most of the cases as information or an indicator because already processed to be semantically consistent. So, this data provides rich information of system operation (without adding sensors even if some events are built from CM) and its degradation process.
The aim of this thesis is to develop advanced prognostics approaches using (discrete) event data for predicting the failure or remaining useful life (RUL) of industrial systems. This PhD program is structured in three major phases: (1)-characterization of relevant events related to different types of items (i.e. component, machine, system) to have the necessary indicators that make sense in terms of a reliable prognostics on this item; (2)- pattern construction and recognition that should allow isolating patterns representative of nominal operation, and those representing item failure progression; and (3)-development of advanced prognostics approaches to predict the RUL of an item from occurrences of patterns that are linked with the failure or degradation of the item. The proposed approaches/models will be tested and validated by experimental dataset or/and real-industrial use cases.
Keywords:
Prognostics, PHM, AI, Data-driven models, Discret event data
Department(s):
| Modelling and Control of Industrial Systems |
Publications: