Applications of Deep Learning in Aerospace and Building Systems
Applications of Deep Learning in Aerospace and Building Systems


Recent advances in machine learning techniques such as deep learning (DL) has rejuvenated data-driven analysis in aerospace and integrated building systems. 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 talk demonstrates using deep belief networks (DBN), deep auto encoders (DAE), deep reinforcement learning (DRL) and generative adversarial networks (GAN) in five 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, (iii) prediction of chiller power consumption in heating, ventilation, and air conditioning (HVAC) systems, (iv) material and structural characterization of aerospace parts, and (v) end-to-end control of high-precision additive manufacturing process.


Amit Surana received his Bachelor’s degree in Mechanical Engineering from Indian Institute of Technology Bombay in 2000, his M.S. in Mechanical Engineering and M.A. in Mathematics both from Pennsylvania State University in 2002 and 2003, respectively, and his PhD in Mechanical Engineering from Massachusetts Institute of Technology (MIT) in 2007. He was awarded the Padmakar P. Lele Outstanding Research and Thesis award at MIT for his PhD thesis work on nonlinear dynamics of three dimensional unsteady fluid flow separation.

Since 2007 he has served in various roles of increasing responsibility at United Technologies Research Center (UTRC), where he is now an Associate Director. During 2007-2009 at UTRC, he worked on problems related to model reduction, estimation and control in distributed parameter systems, uncertainty quantification in large scale dynamic networks, and sensor network control and coordination. His work on scalable uncertainty quantification received the 2015 Technical Excellence Award, the highest individual award at UTRC for contribution to Science & Engineering. In 2016, he was invited by the National Academy of Engineering to attend the US Frontiers of Engineering Symposium. He was awarded the 2017 Grainger Grant by the Grainger Foundation and National Academy of Engineering. Currently, he serves as a principal investigator of projects in the areas of collaborative robotics with emphasis on human machine teams, and data analytics with an interdisciplinary approach combining techniques from dynamical systems, control theory and machine learning.

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