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Communication Dans Un Congrès Année : 2024

Autoencoder-based method for online fault detection in discrete-event class Production Systems

Résumé

In the context of discrete-event systems (DES), the terms detection and diagnosis refer to two distinct stages of handling faults and anomalies. Both steps are critical for ensuring the reliable and safe operation of complex systems. In this paper, we propose the use of autoencoders for fault detection in an automated production system with sensors and actuators delivering discrete binary signals that can be modeled as DES. We train an autoencoder exclusively on data representing normal behavior. The model learns to encode typical patterns and reconstruct input data with low loss. A predetermined threshold, determined by the characteristics of the training data, is set for the reconstruction error. During normal behavior, the autoencoder is expected to achieve low reconstruction error below this threshold. When a fault occurs, the autoencoder strives to accurately reconstruct faulty data, leading to a higher error. The detection of a reconstruction error exceeding the threshold signals a potential fault in the system. The results of applying our method to the Factory IO software sorting system demonstrate the significant contribution and the interest of this method for detecting faults.
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Dates et versions

hal-04609395 , version 1 (12-06-2024)

Identifiants

  • HAL Id : hal-04609395 , version 1

Citer

Ramla SADDEM, Dylan Baptiste, Ange Patrick Wabo Teingua. Autoencoder-based method for online fault detection in discrete-event class Production Systems. IFAC Workshop on Discrete Event Systems, Apr 2024, Rio de Janeiro (BR), Brazil. ⟨hal-04609395⟩

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