Servo Motor Predictive Maintenance by Kafka Streams and Deep Learning Based on Acoustic Data

Servo motors, which are critical for high-precision industrial applications, require predictive maintenance to minimize downtime, aligning with Industry 5.0’s human-centric manufacturing. This study presents a system for Delta servo motors using acoustic data. An ESP32 LyraT module streams audio via HTTP to a server, which forwards it to Apache Kafka. Convolutional neural networks (CNNs) detect anomalies; Statistical Process Control (SPC) identifies early faults; and ARIMA, LSTM, and Prophet forecast maintenance. A device architecture with IP-based device ID and a GUI supports monitoring. Experiments with an ESP32 LyraT (Espressif Systems, Shanghai, China) monitoring Delta ASDA-A3 motors (Delta Electronics, Taipei, Taiwan) over 72 h achieved 91% anomaly detection accuracy for anomalous sounds, 84% early fault detection, and LSTM forecasting of MSE trends with MAE 0.0078 for 24 h predictions. The system supported 32 kB/s with <1% packet loss. The system offers accurate monitoring, advancing Industry 5.0. Future work will include vibration data and web dashboards.