Internet of Things Fault Detection and Classification via Multitask
  Learning
Mohammad Arif Ul Alam
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.