Internet of Things Fault Detection and Classification via Multitask Learning
This work addresses fault management in IIoT applications, offering incremental improvements for industrial monitoring systems.
The paper tackled fault detection and classification in Industrial Internet of Things (IIoT) systems by proposing SMTCNN, which achieved a 3.5% improvement in specificity and significant gains in precision, recall, and F1 scores compared to existing methods.
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.