Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
In the public works environment, avoiding the 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 one that is unavailable, all 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.
In order to reduce the number of breakdowns, our goal is to develop a system of predictive maintenance, comparably to what can be used in the industry, using the telematic data that the machines produce.
After testing different “data-driven” approaches and given the complexity and diversity of the breakdowns that can occur, we decided to focus on one specific component: the hydraulic system of crawler excavators, using an expert-based approach with BEKEE to build a Bayesian network representing the health of a hydraulic system.
Yann Corriou Data Scientist Charier S.A.S. yann.corriou@charier.fr
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.