Inductive Graph Neural Network for Virtual Vibration Sensor Reconstruction in PMSM Powertrain

Effective condition monitoring of motors is crucial in diverse electric powertrain systems, including applications in electric transportation and nuclear power plants. Vibration analysis, a key component of motor condition testing, aids in identifying equipment failures, assessing operational status, and guiding preventive maintenance. However, achieving high evaluation accuracy while minimizing the number of vibration sensors to reduce operational and maintenance costs poses a significant challenge. This article introduces a novel approach for vibration testing using a spatial–spectral-based inductive graph neural network. The proposed algorithm focuses on mining vibration sensor-based clusters to reveal spatial connectivity and spectral correlation patterns. It efficiently aggregates and extracts features from sensor graph signals near the target location, subsequently reconstructing vibration signals using convolutional networks to create a virtual sensor. To validate the effectiveness of the proposed method, experimental verification was conducted on a 21 kW interior permanent magnet synchronous motor testing rig equipped with Brüel and Kjær's vibration sensing equipment. The results demonstrate the algorithm's ability to enhance evaluation accuracy and reliability. This innovative approach not only contributes to the field of motor condition monitoring but also addresses the challenge of minimizing the number of vibration sensors, thereby reducing manual operation and maintenance costs associated with sensor networks.