Ph. D. Project
Title:
AI-based machine learning and reasoning for predictive maintenance in Industry 4.0
Dates:
2023/08/28 - 2026/08/27
Student:
Supervisor(s):
Description:
Context:
The PhD student will be recruited at CRAN laboratory to develop his PhD in the framework of the Horizon Europe project MODAPTO (Modular manufacturing and distributed control via interoperable digital twin) which aim to develop and deploy modular and reconfigurable manufacturing systems leveraging production modules enhanced by distributed intelligence via interoperable Digital Twins (DTs) based on industrial standards. At the same time, MODAPTO materializes the benefits of global production view by enabling collective intelligence within modular production schemes for effective module and production line design, reconfiguration, and decision support for, among other, predictive maintenance.
Within the MODAPTO framework, the objective of this PhD is to develop AI-based predictive maintenance for industry 4.0 from heterogeneous knowledge. This algorithm will be validated on use cases of the pilot sites of the project, including SEW USOCOME. As active member of the CRAN team, the PhD student will be involved in the European project by participating to project development, meetings, writing deliverables, presenting progress and results, etc.
Research topic: scientific context and goals
In manufacturing system, maintenance plays a key role to sustain the system within its nominal operation space mainly by anticipating failures (e.g. predictive maintenance). Indeed, maintenance optimization aims to control optimally, and at lowest possible maintenance costs, Key Performance Indicators of the system both related to conventional performances (e.g. productivity, reliability), and emerging ones (e.g. sustainability). In the context of industry 4.0, manufacturing systems are facing to, not only the new ways of producing goods and services (e.g., more flexible/reconfigurable), but also the activities, methods and tools used to maintain industrial equipment and facilities for promoting required agility and resilience. Particularly, with the development of Cyber-Physical Systems (CPS)/Cyber-physical-Production Systems (CPPS) concept, it is appearing massive digital transformation providing huge data related to the health state of CPS/CPPS components and the operational conditions of the system, which can help, thanks to advanced AI techniques (e.g. reinforcement learning or deep learning), to better detect abnormal behaviours of equipment (diagnosis), predict future failure modes (prognosis) and support, by advance, maintenance decisions (proactive decision-making). However, although many advanced AI techniques are now available, their application in prognostics and maintenance decision-making is still limited and remains widely open. Indeed, usually, AI techniques are ad hoc designed in relation for a determined production system. Nevertheless, CPPS, in the frame of reconfigurable manufacturing system, can change their structure to accommodate new production requirements. As such, classical AI-based approach for predictive maintenance must be enhanced with reasoning capabilities aiming to embed knowledge about system structure and constraint enabling the algorithms to cope with such changes.
To face this issue, the aim of this Ph.D. is to develop AI-based machine learning and reasoning for predictive maintenance in industry 4.0. The work developed will provide an effective and efficient solution to the current challenge in predictive maintenance on the use of AI techniques to optimize maintenance actions in dynamic and proactive way (to do the right action, at the right place and in just in time) for reconfigurable manufacturing process leveraging reasoning capabilities based on knowledge formalisation. In that way, the PhD program is structured in three major phases:
Phase 1 - Modelling and formalization of heterogeneous knowledge in predictive maintenance for reconfigurable manufacturing systems: The aim is to define, from the concept of CPS (Cyber-Physical Systems)/CPPS (Cyber-Physical Production Systems) and the implementation process of a preventive maintenance approach, the main features to be associated to the predictive maintenance of CPS/CPPS systems. Then, from these features, the second objective of this phase is to model the concepts and their relation associated to predictive maintenance of CPS/CPPS system. The result should be a knowledge formalisation (e.g. ontologies will be considered as a tool) of the domain that will be used for maintenance decision-making developed in phase 2. This knowledge formalisation allow to provide semantic not only to the production system and support system configuration and constraint data but also to the current data describing the state of the system and its environment leading to a knowledge base (i.e. data + ontology).
Phase 2 - Development of AI-based machine learning and reasoning approach for predictive maintenance decision-making: The proposed models will be built on a set of appropriate decision rules and advanced AI algorithms (e.g. reinforcement learning) which allow to learn the most relevant decision rules to deal with the description of the system and the support system given by the knowledge base. In that way, the proposed AI-based decision approaches should enable not only providing optimal maintenance planning taking into account both the requirements associated with the maintained system (reconfigurable architecture, multi-function, etc.) and its support one (e.g. spare parts, maintenance skill) but also to be able to update efficiently the maintenance planning in a dynamic context (e.g. reconfigurable structure, multi-function/service, change in support system, occurrence of new maintenance opportunities/constraints, etc.).
Phase 3 - Validation of the proposed models/approaches on the pilot case studies: The models/approaches proposed in phases 1 and 2 will be applied to the industrial case studies of the project, including SEW USOCOME. Several performance metrics will be selected to evaluate the performance of the proposed models/approaches. A study on adjustment/validation of the proposed models/ approaches should be also investigated. This phase will allow to underline, from the assessments and adjustments done, the advantages and disadvantages of such approaches for supporting proactive maintenance decision-marking.
The PhD student will be recruited at CRAN laboratory to develop his PhD in the framework of the Horizon Europe project MODAPTO (Modular manufacturing and distributed control via interoperable digital twin) which aim to develop and deploy modular and reconfigurable manufacturing systems leveraging production modules enhanced by distributed intelligence via interoperable Digital Twins (DTs) based on industrial standards. At the same time, MODAPTO materializes the benefits of global production view by enabling collective intelligence within modular production schemes for effective module and production line design, reconfiguration, and decision support for, among other, predictive maintenance.
Within the MODAPTO framework, the objective of this PhD is to develop AI-based predictive maintenance for industry 4.0 from heterogeneous knowledge. This algorithm will be validated on use cases of the pilot sites of the project, including SEW USOCOME. As active member of the CRAN team, the PhD student will be involved in the European project by participating to project development, meetings, writing deliverables, presenting progress and results, etc.
Research topic: scientific context and goals
In manufacturing system, maintenance plays a key role to sustain the system within its nominal operation space mainly by anticipating failures (e.g. predictive maintenance). Indeed, maintenance optimization aims to control optimally, and at lowest possible maintenance costs, Key Performance Indicators of the system both related to conventional performances (e.g. productivity, reliability), and emerging ones (e.g. sustainability). In the context of industry 4.0, manufacturing systems are facing to, not only the new ways of producing goods and services (e.g., more flexible/reconfigurable), but also the activities, methods and tools used to maintain industrial equipment and facilities for promoting required agility and resilience. Particularly, with the development of Cyber-Physical Systems (CPS)/Cyber-physical-Production Systems (CPPS) concept, it is appearing massive digital transformation providing huge data related to the health state of CPS/CPPS components and the operational conditions of the system, which can help, thanks to advanced AI techniques (e.g. reinforcement learning or deep learning), to better detect abnormal behaviours of equipment (diagnosis), predict future failure modes (prognosis) and support, by advance, maintenance decisions (proactive decision-making). However, although many advanced AI techniques are now available, their application in prognostics and maintenance decision-making is still limited and remains widely open. Indeed, usually, AI techniques are ad hoc designed in relation for a determined production system. Nevertheless, CPPS, in the frame of reconfigurable manufacturing system, can change their structure to accommodate new production requirements. As such, classical AI-based approach for predictive maintenance must be enhanced with reasoning capabilities aiming to embed knowledge about system structure and constraint enabling the algorithms to cope with such changes.
To face this issue, the aim of this Ph.D. is to develop AI-based machine learning and reasoning for predictive maintenance in industry 4.0. The work developed will provide an effective and efficient solution to the current challenge in predictive maintenance on the use of AI techniques to optimize maintenance actions in dynamic and proactive way (to do the right action, at the right place and in just in time) for reconfigurable manufacturing process leveraging reasoning capabilities based on knowledge formalisation. In that way, the PhD program is structured in three major phases:
Phase 1 - Modelling and formalization of heterogeneous knowledge in predictive maintenance for reconfigurable manufacturing systems: The aim is to define, from the concept of CPS (Cyber-Physical Systems)/CPPS (Cyber-Physical Production Systems) and the implementation process of a preventive maintenance approach, the main features to be associated to the predictive maintenance of CPS/CPPS systems. Then, from these features, the second objective of this phase is to model the concepts and their relation associated to predictive maintenance of CPS/CPPS system. The result should be a knowledge formalisation (e.g. ontologies will be considered as a tool) of the domain that will be used for maintenance decision-making developed in phase 2. This knowledge formalisation allow to provide semantic not only to the production system and support system configuration and constraint data but also to the current data describing the state of the system and its environment leading to a knowledge base (i.e. data + ontology).
Phase 2 - Development of AI-based machine learning and reasoning approach for predictive maintenance decision-making: The proposed models will be built on a set of appropriate decision rules and advanced AI algorithms (e.g. reinforcement learning) which allow to learn the most relevant decision rules to deal with the description of the system and the support system given by the knowledge base. In that way, the proposed AI-based decision approaches should enable not only providing optimal maintenance planning taking into account both the requirements associated with the maintained system (reconfigurable architecture, multi-function, etc.) and its support one (e.g. spare parts, maintenance skill) but also to be able to update efficiently the maintenance planning in a dynamic context (e.g. reconfigurable structure, multi-function/service, change in support system, occurrence of new maintenance opportunities/constraints, etc.).
Phase 3 - Validation of the proposed models/approaches on the pilot case studies: The models/approaches proposed in phases 1 and 2 will be applied to the industrial case studies of the project, including SEW USOCOME. Several performance metrics will be selected to evaluate the performance of the proposed models/approaches. A study on adjustment/validation of the proposed models/ approaches should be also investigated. This phase will allow to underline, from the assessments and adjustments done, the advantages and disadvantages of such approaches for supporting proactive maintenance decision-marking.
Keywords:
predictive maintenance, AI, optimization, machine learning and reasoning
Department(s):
| Modeling and Control of Industrial Systems |
