IoT-Based Predictive Maintenance for AC Motors in Water Treatment Plants Using Multi-Sensor Data and LSTM Networks with GAN Augmentation - Angga Debby Frayudha; Aris Puja Widikda (Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY, 2025)
AC motors are critical assets in water treatment plants because they operate continuously to drive key processes. Reactive or schedule-based maintenance can miss early degradation and increase the risk of unplanned downtime. This study presents a field implementation of an Internet of Things (IoT)-based predictive maintenance system in a WTP. The system integrates vibration, temperature, and rotational speed (RPM) sensors with a cloud-based IoT pipeline for real-time data acquisition. Operational data were collected for 30 days from a single motor unit and analyzed using Random Forest and Long Short-Term Memory models. To address limited abnormal-event data, Generative Adversarial Network (GAN)-based augmentation was applied during training. The results show that LSTM performed more consistently than Random Forest; after augmentation, the F1-score improved from 0.92 to 0.95. The monitoring data also captured warning-level changes during operation, including vibration up to 3.9 mm/s, temperature up to 95 °C, and rotational speed dropping to around 1420 RPM, which may indicate abnormal operating conditions requiring inspection. Given the single-unit scope and short duration, the findings are reported as an initial implementation case study. Nevertheless, the work demonstrates the feasibility of a low-cost IoT-based monitoring and prediction framework to support maintenance decisions in WTP operations.