Cost-Effective Real-Time Data Acquisition in SCADA Systems Using Edge Devices and Deep Learning for Legacy Equipment Integration

SCADA systems are widely used in industrial environments for centralized data monitoring and control, but their effectiveness is often limited to equipment with modern sensors. Many critical instruments, such as analog gauges and digital displays, still rely on manual readings due to the lack of digital interfaces, leading to inefficiencies and potential human error. Retrofitting these legacy instruments with IoT-enabled solutions is costly and disruptive. To address this challenge, we propose a cost-effective real-time data acquisition system that integrates legacy equipment into the SCADA systems using edge devices and deep learning. Our approach utilizes cameras and low-cost sensors to capture instrument readings, leveraging machine vision and indirect measurement techniques for accurate data extraction. Implemented on a Raspberry Pi 4B, the system employs the Fast-SCNN semantic segmentation model to identify key elements of analog gauges, achieving an average reading error of 1.5% at 2–3 frames per second. For digital meters, the PP-OCRv4 model achieves 95% accuracy on a custom dataset and 85.6% accuracy on Raspberry Pi 4B, operating at nearly 10 frames per second to enhance the reading efficiency. Additionally, an LSTM-based indirect temperature estimation model predicts liquid temperatures from external pipe wall readings, achieving the RMSEs of 2.92 ∘ C and 3.77 ∘ C on the validation and test sets, respectively. These findings highlight a practical and scalable solution for industrial automation, showcasing the potential of deep learning in edge computing for interfaceless instrument data acquisition.