Data-driven Robust Detection of Operational Drifts in Pilot Units

IFPEN aims to become a key player in the triple energy, ecological and digital transition by offering differentiating technological solutions in response to the societal and industrial challenges of energy and climate. The implementation of new methodological approaches that combine data science and experimentation is among the solutions under study to accelerate progress and reduce R&D costs. Learning algorithms based on time series data, which constitute most generated data, are currently gaining momentum in the literature, whether supervised or unsupervised (ARIMA, LSTM…).
The field of Prognostics and Health Management (PHM) is of particular interest. It is a discipline that focuses on the degradation mechanisms of systems to estimate their health status, anticipate failures, and optimize maintenance. For instance, on a pilot plant, it is very challenging to obtain data during operational problems, which are becoming more frequent due to the high variability of loads to process and operational conditions to explore. This thesis aims to overcome these challenges by exploring data-driven methodologies. The choice of this approach is justified by the extensive diversity of data available at IFPEN. Additionally, new experimental trials may be conducted as a complementary measure.

Keywords: Data science, machine learning, prognostics and health management PHM, deep learning, Process Systems Engineering

• Academic supervisor    Dr Alexandre VOISIN, CRAN, ORCID : 0000-0002-4637-6826
• Doctoral School    ED77 : Automatique et traitement du signal, Université de Lorraine
• IFPEN supervisor    Dr Victor COSTA, ORCID :0000-0002-9723-8538
• PhD location    IFPEN, Solaize, France  
• Duration and start date    3 years, starting in the fourth quarter 2024 (Novembre 4)
• Employer    IFPEN
• Academic requirements    University Master degree in applied mathematics, informatics, data science or chemical engineering / automation with a strong interest for data science.    
• Language requirements    English level B2 (CEFR)    
• Other requirements    Python programming

To apply, please send your cover letter and CV to the IFPEN supervisor indicated below.