Deep clustering variational network for helicopter regime recognition in HUMS

Abstract

The helicopter health and usage monitoring system is the core system to ensure its operational safety. Flight regime recognition is a key pilot task therein that affects subsequent decision-making. However, the current research on this topic has not aroused wide attention. Taking advantage of deep learning, a powerful pattern recognition tool, we proposed a deep clustering variational network to serve the helicopter regime recognition task. Through explicit feature distribution constraints and clustering loss function, we have made a clearer decision boundary and more significant category differences, thus achieving accurate recognition results. Two case studies show that deep clustering variational network can effectively recognize the regimes by utilizing vibration signals in time between overhaul experiments or online flight parameters.