Using Bayesian Networks to Prevent Breakdowns on Hydraulic Systems of Crawler Excavators

Using Bayesian Networks to Prevent Breakdowns on Hydraulic Systems of Crawler Excavators

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.

Presentation Video

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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