Real-time anomaly detection in electric motor operation noise

Abstrak

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.