Real-Time Production Monitoring approach for Smart Manufacturing with Artificial Intelligence techniques

Q.T Nguyen (1) , H D Nguyen , Kim Phuc Tran (2, 3) , P. Castagliola (4, 5) , E Frénod (1)

1 LMBA - Laboratoire de Mathématiques de Bretagne Atlantique
2 Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
3 GEMTEX - Génie et Matériaux Textiles
4 LS2N - Laboratoire des Sciences du Numérique de Nantes
5 LS2N - équipe SLP - Systèmes Logistiques et de Production

Production monitoring in real-time is a very important problem in smart manufacturing. It helps enterprises to timely detect abnormalities in the production process and then guarantee the product quality and reduce waste. In this paper, we develop a novel method to monitor the real-time production based on the Convolution Neural Network and the Support Vector Data Description algorithm. The numerical result shows that our proposed method leads to high efficient on the testing data.

Mots clés: One-class classification; SVDD; Real-time monitoring; Anomaly detection; CNN; transfer learning

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Q.T Nguyen, H D Nguyen, Kim Phuc Tran, P. Castagliola, E Frénod. Real-Time Production Monitoring approach for Smart Manufacturing with Artificial Intelligence techniques. ISSAT International Conference on Data Science in Business, Finance and Industry, Jul 2019, Da Nang, Vietnam. pp.100-103. ⟨hal-02268131⟩