Data-driven prognostics are instrumental in enabling anomaly detection, sensor estimation and prediction in prognostics and health management (PHM) systems. Recent advances in machine learning techniques such as deep learning (DL) has rejuvenated data-driven analysis in PHM. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement is significant from the features learnt from DL techniques as compared to the hand crafted features. This paper proposes using deep belief networks (DBN) and deep auto encoders (DAE) in three different aerospace and building systems applications: (i) estimation of fuel flow rate in jet engines, (ii) fault detection in elevator cab doors using smart phone, and (iii) prediction of chiller power consumption in heating, ventilation, and air conditioning (HVAC) systems.