The document discusses United Technologies Corporation's application of deep learning techniques to problems in aerospace and building systems. Specifically, it discusses using deep belief networks for aircraft sensor diagnostics at Pratt & Whitney and Otis elevators prognostic health monitoring. It also discusses using deep autoencoders for chiller power estimation at Carrier Climate Control systems. The approaches analyzed sensor data using deep learning models to provide diagnostics, predict health issues, and estimate power usage.