Edge AI Based Anomaly Detection for Real Time Monitoring of IoT Enabled Industrial Systems

Marsutiyawan Aji

doi:10.55927/fjst.v5i9.162

Authors

Marsutiyawan Aji PT. Karyatama Solusindo

DOI:

https://doi.org/10.55927/fjst.v5i9.162

Keywords:

Edge AI, IoT, Anomaly Detection, Industrial Monitoring, Autoencoder.

Abstract

Industrial machine failures can reduce productivity and increase maintenance costs, requiring an effective real-time monitoring approach. This study develops an Edge AI-based anomaly detection system for IoT-enabled industrial systems using multi-sensor data consisting of vibration, temperature, and current measurements. The proposed method applies an Autoencoder model deployed on an edge computing device to identify abnormal machine conditions with low latency. The novelty of this study is the integration of lightweight AI inference and multi-sensor analysis for real-time industrial monitoring. Experimental evaluation using 10,000 sensor data samples achieved 96.8% accuracy, 95.9% precision, 96.3% recall, and 96.1% F1-score with a response time of 0.28 seconds. The proposed system supports efficient predictive maintenance applications

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