Optimizing Maintenance and Avoiding Major Breakdowns of Construction Machines

Optimizing Maintenance and Avoiding Major Breakdowns of Construction Machines

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.


In the public works environment, avoiding breakdowns of construction machines is a major challenge. Indeed, this phenomenon can represent a significant economic cost at three different levels. First, we need to pay for the repair of the machine, which is called the direct cost. Then, a breakdown will eventually lead to a delay in the progress of the work or to the need to rent another machine to replace the unavailable one, all of this representing the indirect cost. And finally, a breakdown can also affect the lifetime of a machine, and optimizing this lifetime is a priority when handling a fleet of public works equipment.

To reach our goal, we are developing different Bayesian networks using an expert-driven approach with BEKEE. The presentation will be about the development of our networks, how we use this process to target useful data for our CMMS, and how we plan to include the result of the networks in our CMMS.

Presentation Video

Presentation Slides

About the Presenter

Yann Corriou, Data Scientist, Charier S.A.S.

I studied for five years (2015-2020) at INSA Rennes (engineering school) in the Department of Applied Mathematics. After an internship (2019) and a one-year work-study contract (2019-2020), I am now working full-time as a data scientist at CHARIER, a public works company operating mainly in the West of France.

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