Towards Industry 4.0: Machine malfunction prediction based on IIoT streaming data

• Autorzy: Nikolova Dragana , Lameski Petre , Pires Ivan Miguel , Zdravevski Eftim

Identyfikatory

DOI

10.15439/2023F677

• Języki publikacji: EN

Abstrakty

EN

The manufacturing industry relies on continuous optimization to meet quality and safety standards, which is part of the Industry 4.0 concept. Predicting when a specific part of a product will fail to meet these standards is of utmost importance and requires vast amounts of data, which often is collected from variety of sensors, often reffered to as Industrial Internet of Things (IIoT). Using a published dataset from Bosch, that describes the process at every step of production, we aim to train a machine learning model that can accurately predict faults in the manufacturing process. The dataset provides two years of production data across four production lines and 52 stations. Considering that the data generated from each production part includes 4,264 features, we investigate various feature selection and data preprocessing methods. The obtained results exhibit AUC ROC of up to 0.997, which is remarkable and promising even for real-life production use.

Słowa kluczowe

EN

industrial Internet of Things; industry 4.0; machine malfunction; failure prediction

PL

przemysłowy internet rzeczy; przemysł 4.0; awaria maszyny; przewidywanie awarii

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 291--296
• Opis fizyczny: Bibliogr. 17 poz., wykr., tab.

Twórcy

autor

Nikolova Dragana

• Faculty of Computer Science and Engineering,Ss Cyril and Methodius University, Skopje, Macedonia

autor

Lameski Petre

• Faculty of Computer Science and Engineering,Ss Cyril and Methodius University, Skopje, Macedonia

autor

Pires Ivan Miguel

• Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

autor

Zdravevski Eftim

• Faculty of Computer Science and Engineering,Ss Cyril and Methodius University, Skopje, Macedonia

Bibliografia

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• 16. E. Zdravevski, P. Lameski, V. Trajkovik, A. Kulakov, I. Chorbev, R. Goleva, N. Pombo, and N. Garcia, “Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering,” IEEE Access, vol. 5, pp. 5262–5280, 2017.
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Uwagi

1. This work was partially financed by the Faculty of Computer Science and Engineering at the Ss. Cyril and Methodius University, Skopje, Macedonia. This work is also partially funded by FCT/MEC through national funds and, when applicable, cofunded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020.

2. Main Track Short Papers

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).