Hybrid AI Models for Predictive Maintenance in Industrial IoT

Dr. Akram Khan

Abstract


Predictive maintenance is critical for reducing downtime and costs in industrial settings. This paper presents a hybrid AI model that combines deep learning with traditional statistical methods to predict equipment failures in Industrial IoT (IIoT) environments. The model leverages sensor data for anomaly detection and failure prediction, integrating long short-term memory (LSTM) networks for temporal analysis and Bayesian inference for uncertainty estimation. Case studies from manufacturing and energy sectors demonstrate the model’s efficacy in improving maintenance scheduling and operational efficiency.

References


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