Deep Learning for Machine Health Monitoring
il y a 2 semaines
Position description
**Category**:
- Mathematics, information, scientific, software
**Contract**:
- Internship
**Job title**:
- Deep Learning for Machine Health Monitoring H/F
**Subject**:
- This internship aims to explore advanced Deep Learning techniques, including transfer and few-shot learning, self-supervised representation learning, to enhance the accuracy and generalizability of fault diagnosis in Machine Health Monitoring (MHM) systems.
**Contract duration (months)**:
- 4/6
**Job description**:
- Machine health monitoring (MHM) refers to techniques for assessing the health of rotating machines (e.g. electric motors, gearboxes, tools), with the aim of increasing the lifespan and reduce operating costs. It is also called predictive maintenance, condition monitoring or machine fault diagnosis, etc. A typical data-driven workflow in MHM is composed of: data acquisition, feature extraction, model construction, and decision making. Sensor data is acquired under various working conditions (e.g. rotational speed, load) by measuring vibration acoustic/electrical signals, which are very often unlabeled and exhibit unbalanced characteristics. For example, anomalous data is collected less frequently than normal data; Different types of defects can occur at different rates. An effective machine learning model must cope with data imbalance and scarcity and be able to generalize to new domains (new type of machine, operating conditions, faults, etc.).- Deep learning (DL) has emerged as a powerful tool for MHM, enabling the extraction of meaningful representation from sensor data and the knowledge tranfer to downstream task.This internship aims to explore advanced DL techniques, including transfer and few-shot learning, self-supervised representation learning, to enhance the accuracy and generalizability of fault diagnosis in MHM systems.- objectives:
- Adapt some SOTA methods of self-supervised/transfer/few-shot learning to the con
- text of MHM.- Implement these methods as a complete pipeline, and compare their performance on
an established collection of databases. Eventually proposes improvments.- Integrate the proposed pipeline and contribute to the development of a python package.
- The candicate should have a first or second year master level in fields related to machine learning and signal processing. Good programming skills in Python and experiences in deep learning architectures and libraries (e.g. Tensorflow, Keras) are required.Position location
**Site**:
- Saclay
**Job location**:
- France, Ile-de-France, Essonne (91)
**Location**:
- Gif-Sur-Yvette
**Prepared diploma**:
- Bac+4 - Master 1
**PhD opportunity**:
- Non
General information
**Organisation**:
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas:
- defence and security,
- nuclear energy (fission and fusion),
- technological research for industry,
- fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
- The CEA is established in ten centers spread throughout France**Reference **:2024-30901
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