Dr. Orchard's research work at the Georgia Institute of Technology was the foundation of novel real-time fault diagnosis and failure prognosis approaches based on particle filtering algorithms. He received his PhD and M.S. degrees from The Georgia Institute of Technology, Atlanta, GA, in 2005 and 2007, respectively. He received his B.S. degree (1999) and a Civil Industrial Engineering degree with Electrical Major (2001) from Catholic University of Chile. 
Dr. Orchard is an internationally recognized expert in the field of fault diagnosis and prognosis, Bayesian filtering in dynamic non-linear systems, stochastic processes and sequential Monte Carlo methods.

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My research interests include system identification, fault detection and isolation (FDI), failure prognosis, and statistical process control. Amongst the techniques and approaches that I am familiar with I can mention sequential Monte Carlo methods (Particle Filtering), Kalman filtering, multivariate statistical analysis (PCA and PLS), wavelet analysis, fuzzy expert systems, fuzzy predictive control, neural networks. At the moment, I am focused on uncertainty characterization and management in failure prognostic algorithms using sequential Monte Carlo methods applied to nonlinear, time-varying, and non-Gaussian systems. More information may be found in my publications.