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Journals / Nicel Bilimler Dergisi / 2024 / Cilt: 6 Sayı: 2

On Anomaly Detection for Autonomous Transfer Vehicles in Smart Factories

Akıllı Fabrikalarda Otonom Taşıyıcı Araçlarında Anomali Tespiti

Özlem Örnek(ESKISEHIR OSMANGAZI UNIVERSITY)0000-0002-8775-8695
Efnan Şora Günal(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0001-6236-174X
Ahmet Yazici(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0001-5589-2032
Pages
208–227
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results indicate that the first method provides a higher accuracy whereas the second one offers a better false-negative rate in detecting anomalies regarding ATVs.

📌 3 Key Takeaways

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Objective & Hypothesis:Today, autonomous transfer vehicles (ATVs) have important roles in many smart factories.
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Key Findings & Data:The other method utilizes a 2-stage hybrid approach for anomaly detection.
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Conclusion & Impact:The experimental results indicate that the first method provides a higher accuracy whereas the second one offers a better false-negative rate in detecting anomalies regarding ATVs.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Anomali tespiti · Otonom taşıyıcı araçlar · Akıllı fabrikalar

Abstract

Today, autonomous transfer vehicles (ATVs) have important roles in many smart factories. Therefore, flawless and uninterrupted operation of ATVs is required for the sake of effective production in smart factories. For this reason, it is important to detect anomalies (or, abnormalities) regarding ATVs during the operation. Therefore, this study aims to detect anomalies regarding ATVs so that possible losses during production can be prevented. For this purpose, two novel methods are proposed to detect anomalies for ATVs. The first method employs exhaustive feature selection to obtain the optimal subset of features for detecting anomalies. The other method utilizes a 2-stage hybrid approach for anomaly detection. Four types of anomalies (overdue pick-up delivery activity, unexpected pedestrian density, unexpected vehicle slow-down, and unexpected vehicle behavior) are considered for this work. During the experimental work, a test environment has been established for simulating a smart factory. The experimental results indicate that the first method provides a higher accuracy whereas the second one offers a better false-negative rate in detecting anomalies regarding ATVs.

Özet

Günümüzde otonom taşıyıcı araçların (OTA) birçok akıllı fabrikada önemli rolleri var. Bu nedenle akıllı fabrikalarda etkin üretim için OTA'ların kusursuz ve kesintisiz çalışması gerekmektedir. Bunu sağlamak için OTA'lara ilişkin anomalilerin (veya anormalliklerin) operasyon sırasında tespit edilmesi önemlidir. Bu amaçla bu çalışmada OTA'lara ilişkin anormalliklerin tespit edilerek üretim sırasında olası kayıpların önlenmesi amaçlanmaktadır. OTA'lardaki anomalilerin tespiti için iki yöntem önerilmiştir. İlk yöntem, anomalilerin tespiti için en uygun özellik alt kümesini elde etmek amacıyla kapsamlı özellik seçimini kullanır. Diğer yöntemde anomali tespiti için iki aşamalı hibrit yaklaşım kullanılır. Bu çalışma için dört tür anomali (gecikmiş teslim alma faaliyeti, beklenmeyen yaya yoğunluğu, beklenmeyen araç yavaşlaması ve beklenmeyen araç davranışı) dikkate alınmıştır. Deneysel çalışma sırasında akıllı fabrikanın simüle edilebilmesi için bir test ortamı oluşturulmuştur. Deneysel sonuçlar, OTA’lara ilişkin anomalilerin tespitinde birinci yöntemin daha yüksek doğruluk sağladığını, ikincisinin ise daha iyi bir yanlış negatif oranı sunduğunu göstermektedir.

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Cite This Article

Örnek, Ö., Günal, E. Ş., Yazici, A. (2024). On Anomaly Detection for Autonomous Transfer Vehicles in Smart Factories. Nicel Bilimler Dergisi, Cilt: 6 Sayı: 2, 208–227. https://doi.org/10.51541/nicel.1450906

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