3D Acoustic Heat-Maps for Transformer Monitoring Applications

We present a novel approach for measuring and representing acoustic emissions of transformers. The representation of locationbased 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.