Internet of Things Fault Detection and Classification via Multitask Learning

TL;DR

This paper introduces SMTCNN, a multitask learning model for fault detection and classification in industrial IoT systems, demonstrating improved accuracy and reliability on real-world data.

Contribution

The paper develops and evaluates a novel multitask deep learning model, SMTCNN, tailored for fault detection and classification in IIoT environments, addressing practical deployment challenges.

Findings

SMTCNN outperforms existing methods in precision, recall, and F1 score.

The system effectively detects and classifies 11 fault categories in real-world data.

Superior specificity of 3.5% indicates high fault detection accuracy.

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.