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Predictive maintenance (Pilot)

Type of course:

Digital learning, Lesson

Language:

EN

Duration:

5 minutes

Proficiency:

Intermediate

Target:

Professionals

              In the previous lesson, we looked at the challenges of industrial maintenance. In particular, we learned to distinguish between corrective maintenance, curative maintenance and preventive maintenance. We also saw the difference between systematic and condition-based preventive maintenance.

              Now we’re going to understand how predictive maintenance, by virtue of its proactivity, is a revolution in relation to the classic framework of historical maintenance, and how it opens the way to new possibilities in Industry 4.0.

About The Author

Killian Niel is a data scientist and a pedagogical engineer at the Ecole de Génie Industriel of Grenoble INP in France. He is particularly attracted to the application of machine learning to solve industrial problems. He is interested in confidentiality issues in the field of artificial intelligence and carried out a research and development project about privacy-preserving machine learning techniques for a SME.


Learning outcomes

  1. By the end of this lesson, students will be able to explain the interest of predictive maintenance.
  2. By the end of this lesson, students will be able to apply the challenges of return on investment.
  3. By the end of this lesson, students will be able to define when predictive maintenance is appropriate.

Topics

Digital Transformation, Machine Learning, Artificial Intelligence (AI)

Tags

Machine learning, manufaturing data

Content created in 2024
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