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Communication Dans Un Congrès Année : 2023

Machine Learning Method Applied Online on a Waste Sorting Plant for Early Jam Detection

Résumé

In waste sorting plants, the operators endure conveyor belts jams because sorting waste is not easy due to its complex and unpredictable nature (shapes, weight, tangle, humidity, dirt …). Their job is also hard with patrolling up to 12 km per shift, and having to manually clear the jammed waste. In addition, preventing jams means more waste sorted, more materials to reuse and less to incinerate or to bury. To answer a current non-effective solution to detect jams on conveyor belts in waste sorting plants, a Machine Learning algorithm, the k-Nearest Neighbors is implemented online on site on different conveyor belts at the same time, to detect jams before the conveyor belts stop. This paper describes the different enhancements to a previous version of the k-Nearest Neighbors model by adding normalization, rejection, adaptation of the training set, on site implementation and results.
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Dates et versions

hal-04109678 , version 1 (30-05-2023)

Identifiants

  • HAL Id : hal-04109678 , version 1

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Calliane You, Olivier Adrot, Jean-Marie Flaus. Machine Learning Method Applied Online on a Waste Sorting Plant for Early Jam Detection. 22nd World Congress of the International Federation of Automatic Control IFAC World Congress 2023, Jul 2023, Yokohama, Japan. ⟨hal-04109678⟩
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