Internet of Things Fault Detection and Classification via Multitask Learning

Mohammad Arif Ul Alam

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arXiv · 2307.01234

Internet of Things Fault Detection and Classification via Multitask Learning

Mohammad Arif Ul Alam

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Abstract

This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications. The study addresses challenges in data collection, annotation, algorithm development, and deployment. Using a real-world IIoT system, three phases of data collection simulate 11 predefined fault categories. We propose SMTCNN for fault detection and category classification in IIoT, evaluating its performance on real-world data. SMTCNN achieves superior specificity (3.5%) and shows significant improvements in precision, recall, and F1 measures compared to existing techniques.

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Mohammad Arif Ul Alam. 2023-07-03. Internet of Things Fault Detection and Classification via Multitask Learning. https://arxiv.org/abs/2307.01234

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