3D Acoustic Heat-Maps for Transformer Monitoring Applications

Abstract

We present a novel approach for measuring and representing acoustic emissions of transformers. The representation of location-based acoustic emissions enables improved monitoring of transformers, e.g., to detect and predict anomalies and failure events. Here, we introduce 3D acoustic heatmaps to visualize the sound emission patterns of a transformer. We use a combined sensing approach consisting of a 3D point cloud, a microphone array, and beamforming algorithms to generate the distributed representation of acoustic emissions over the entire surface of the transformer. In a further step, we intend to apply machine learning methods to the generated data to enable early fault prevention and predictive maintenance.