Volume 18, Number 3

Edge-AI Enabled Fault Detection and Root Cause Analysis in Industrial Motors using Multimodal Sensor Data

  Authors

Jayashree Kulkarni¹, Ramesh Kagalkar² and Nandini Sidnal³, ¹Graphic Era Deemed to be University, India, ²Nagarjuna College of Engineering and Technology, India, ³Torrens University, Australia

  Abstract

This article presents an Edge-AI enabled hybrid deep learning framework for real-time fault detection and root cause analysis in industrial motors using multimodal sensor data, containing vibration, temperature, and current signals. The proposed system leverages a CNN-LSTM model deployed on edge devices to accurately classify operational states into normal, minor, and major faults. A dataset comprising 15,000 labeled samples collected from real-world industrial setups and augmented through techniques such as SMOTE and time-warping was used for training and evaluation. In the implementation,the CNN component captures spatial patterns in sensor data, while the LSTM layer models temporal dependencies, enabling effective fault diagnosis. The proposed hybrid model achieved superior performance with 96.8% accuracy, 97.2% precision, 96.5% recall, and an F1-score of 96.8%, along with a low inference latency of ≤198 ms, demonstrating suitability for real-time edge deployment. Comparative analysis against CNN-only and LSTM-only models confirms the hybrid architecture’s advantage in fault sensitivity and prediction reliability. Additional insights from confusion matrix analysis, ROC-AUC evaluation, and fault-wise performance metrics validate the model’s robustness. The system also incorporates TinyML-based optimizations and lightweight messaging for efficient edge computing, making it a scalable solution for predictive maintenance in Industry 4.0 applications.

  Keywords

Edge-AI, CNN-LSTM, Predictive Maintenance, Industrial Motors, Fault Detection, Multimodal Sensors