Stage M2 / Ingénieur
Predictive maintenance, essential for the industry 4.0, allows for the prevention of costly breakdowns by anticipating malfunctions. One way of planning maintenance operations is by detecting anomalies based on historical and realtime data. However, the scarcity of data related to anomalies limits the effectiveness of supervised approaches, highlighting the relevance of unsupervised methods. Time-frequency analysis [2, 5], which decomposes non-stationary signals into temporal and spectral components, provides the opportunity to detect subtle variations in industrial systems.
The objective of this internship is to leverage time-frequency characteristics to formulate a regularized optimal transport problem, leading to the development of an unsupervised anomaly detection algorithm. Indeed, optimal transport [1, 6] is known for its sensitivity to anomalies, and the idea is to exploit this sensitivity in the time-frequency domain to identify the types of considered anomalies [4]. The data for this study will be generated from a real industrial environment using the IT’mFactory platform [3].
To implement our unsupervised algorithm for anomaly detection, we will focus on the following tasks:
• Modeling the behavior of coefficients in the time-frequency plane.
• Mathematical formulation of the anomaly detection as an optimization problem.
• Definition of a decision threshold based on optimal transport distance.
Basic information
• Internship duration: 5 months
• Starting date: as soon as possible and no later thanMarch 31, 2024
• Location: École desMines de Saint-Étienne (EMSE), Institut Henri Fayol, Saint-Étienne, France
• Indemnities: Legal amount (https://www.service-public.fr/particuliers/vosdroits/F32131)
• Supervisors: Marina Krémé marina.kreme@emse.fr, Arthur Kramer arthur.kramer@emse.fr, Thomas Galtier thomas.galtier@emse.fr
Candidate profile
• 2nd-year of MSc and/or 3rd-year of an engineering school,
• Strong background in applied mathematics,
• Strong programming skills in Python
• Proficiency in the English language
• Skills in signal processing will be highly appreciated.
contact pour candidater : Marina Krémé marina.kreme@emse.fr
