Industrial Motor Anomaly Detection: Data System Ready in Under a Week

In industrial environments, numerous electric motors of the same model operate in varied processes, introducing data variability from changes in RPM and production settings. Addressing production delays due to motor malfunctions necessitates a predictive system grounded in data analysis. This system must efficiently manage the lack of historical motor data, facilitating accurate predictions for motor replacement and maintenance timelines.

IoT wireless sensors were implemented to collect data from a wide array of industrial motors, both small and large, by leveraging the MRX Motor solution for reliable data acquisition. In parallel, an MLOps platform was developed to instantly incorporate data that reflects the unique characteristics of each motor.

Within a brief 7-day period, a stable data collection infrastructure was established, leading to the development of a deep learning model. This model effectively identifies data patterns to predict motor anomalies, ascertain failure times, and locate faults. Furthermore, an AI operational framework (MLOps) that integrates real-time data into continuous model retraining and redeployment, allows the system to scale and significantly reduce downtime caused by motor failures, demonstrating a swift and comprehensive approach to industrial challenges.