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Communication Dans Un Congrès Année : 2023

Jam detection in waste sorting plant based on an Autoencoder Neural Network

Résumé

By 2060, the estimated amount of worldwide plastic waste will triple and half of it will be sent to landfill. Yet, waste sorting plants that treat this waste face an unsolved recurrent main problem that is the occurrence of jams in conveyor belts. These jams limit the quantity of waste sorted, whereas waste sorting plants have a constraint to sort a certain amount of waste per week. The main causes of these jams are the complexity and variability of the composition of the waste flow (dirt, humidity, etc.). Therefore, a method based on an autoencoder artificial neural network is used to detect potential jams on conveyor belts in waste sorting plants. The method and results on real industrial data are explained and allow concluding on the feasibility of using an autoencoder model to detect potential jams.
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Dates et versions

hal-04109710 , version 1 (30-05-2023)

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  • HAL Id : hal-04109710 , version 1

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Calliane You, Olivier Adrot, Jean-Marie Flaus. Jam detection in waste sorting plant based on an Autoencoder Neural Network. 33nd European Safety and Reliability Conference ESREL2023, Sep 2023, Southampton, United Kingdom. ⟨hal-04109710⟩
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