CEDoc-UM6P-Aimovement-Combining Datadriven and degradation models for optimal maintenance scheduling
il y a 2 semaines
Located in the heart of the Kingdom's capital at Technopolis, University Mohammed VI Polytechnic (UM6P), is an internationally-oriented higher education institution, which is committed to an educational system based on the highest standards of teaching and research in fields crucial to the sustainable economic development of Morocco and Africa.
Ai movement, the International centre for Artificial Intelligence in Morocco, attached to UM6P, is a center of excellence in Artificial Intelligence, which aims to foster the emergence of Moroccan and African know-how in Artificial Intelligence and Data Science. It is UNESCO's reference center for artificial intelligence in Africa. Ai movement's goal is to develop a trustworthy, ethical, responsible and inclusive AI on the African continent.
Description :
This PhD thesis aims to leverage machine learning and data analytics to predict equipment failures, optimize maintenance schedules, and enhance the efficiency and reliability of industrial operations. The thesis will address two key challenges: 1. Handling Uncertainty in Data: In practice, the data used to feed predictive models is often plagued by various biases, such as missing values, measurement errors, or unstable sensors. To better manage these uncertainties, this research adopts a stochastic optimization approach, allowing the development of predictive and scalable maintenance strategies that remain robust in uncertain environments. 2. Development of optimization maintenance scheduling: The implementation of these stochastic optimization models will incorporate a rich set of constraints. These will include, among others, resource limitations, which will be modeled within the same convex hull framework as production and safety costs. The research will involve analyzing datasets to identify degradation patterns in components using time series data. A central goal is to accurately estimate the degradation of the components and predict potential failures. To this end, Remaining Useful Life (RUL) prognostics will be developed using machine learning algorithms and/or stochastic modeling. These RUL predictions will then be integrated into optimization models to schedule maintenance activities (e.g., inspections or replacements). The objectives are to minimize maintenance costs, extend component lifespan, and reduce the likelihood of unexpected failures. Performance evaluations of the RUL models and corresponding optimization strategies will be carried out through simulations. Additionally, the study will address combinatorial and dynamic optimization problems, supported by machine learning techniques. A promising direction within this scope is the integration of online learning leveraging real-time monitoring data to update degradation models and maintenance scheduling, aligning with the "smart predict-then-optimize" framework. 1
2 Admission Criteria :
The PhD position is available at Ai movement, the International Center for Artificial Intelligence of Morocco of UM6P. Applicants with excellent academic credentials must be holders of a Master's, an engineering or an equivalent recognized degree with good skills in applied mathematics, in relation to optimization, operations research, and machine learning. The candidate should also be excellent in programming in (Python, Java or C ), should have soft skills, and be fluent in English and French languages. Letters of recommendation are welcome.
Ai movement – UM6P
Université Mohammed VI Polytechnique de Rabat
Technopolis, Rocade de Rabat, Maroc
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