IoT-Enabled Real-Time Energy Consumption Anomaly Detection and Diagnosis for Automotive Paint Drying System

The energy-intensive automotive industry requires sophisticated energy management systems to improve energy efficiency. In automotive workshops, paint drying systems are a significant energy consumer, necessitating real-time monitoring and control to minimize energy waste and potentially prevent system malfunctions. Thus, this study proposed a novel real-time energy consumption anomaly detection and diagnosis methodology (eAnoD) for automotive paint drying systems to enhance their energy efficiency and operational safety. Specifically, an architecture combining a temporal convolutional network and graph attention network (TCN-GAT) was devised to extract spatiotemporal features from multidomain data, including energy consumption, equipment parameters, production states, and environmental conditions. A hybrid neural network combining a backpropagation neural network (BPNN) and variational autoencoder (VAE) was constructed to enable the prompt identification of energy consumption deviations. Furthermore, an anomaly grading method integrating combination weighting and cloud modeling techniques was developed to evaluate anomaly severity, facilitating targeted maintenance and proactive risk prevention. A real-world case study was conducted in a new-energy vehicle factory to validate the effectiveness and practicality of the proposed methodology and demonstrate its potential for energy saving and risk mitigation in automotive manufacturing. This study is expected to serve as a reference for practical implementation and generate new ideas for academic exploration.