C. Philip Chen, Ph.D..
Professor and Chair
Department of Electrical and Computer Engineering
Educational Background:
Ph.D., Purdue University
Areas of Research Interest:
Dr. Chen's research interests and projects, supported by the NSF, Air Force Office Scientific Research, U.S. Air Force, Office of Naval Research, and NASA, include intelligent systems, adaptive systems, theoretic development and application of neural networks, fuzzy-neural-genetic adaptive systems, communication networks, robotics and automation, and CAD/CAM. As the PI, his current research projects, funded by NASA and AFOSR, include engine health monitoring, life extending control, design of computational intelligent systems, networking and video data indexing, retrieval, and communications. Dr. Chen has been working at the U.S. Air Force Wright Laboratory as a senior research fellow sponsored by the National Research Council. He also has been a research faculty fellow for Wright-Patterson Air Force Base and NASA Glenn Research Center for several years working on engine health monitoring, materials property prediction, nondestructive testing (NDT), and manufacturing related projects.
Research Descriptions:
Neural and Fuzzy Theory, Technology, and Robotics/Video/Engineering/Science Applications
Neural networks and fuzzy systems have been regarded as the main branches of computational intelligence (or soft computing). Most of Dr. Chen's research discoveries have been focused on the development of theories and the design of systems and algorithms for specific applications. Recently, the designed neural fuzzy systems have been applied to camera motion classification by extracting and processing motion vector information from MPEG video. A research direction in this area is to explore the mathematical foundation of integrating this hybrid system. As for emerging technology application, integrating Bayesian learning with computational intelligence is a future prosperous area. Obviously, there exist very bright future perspectives in exploring soft computing to engineering, science, and robotics/manufacturing applications. Dr. Chen's research area is to exploit the use of Bayesian/filtering methods and computational intelligence for sensor validation/fusion that can be applied to robots for localization and navigation.
Health Monitoring for Aircraft Engines
Engine health monitoring is a complicated process involving a variety of factors. Recently, the health monitoring of the engine has been shown to be sensitive to the derivation of the creep/rupture of the state of the structure. Nonetheless, with proper sensors, a wealth of aircraft engine data is available from many sources including on-board measurement, operating histories, and component models. The proliferation of data makes it possible to build probabilistic models that forecast the remaining life of a structure or component rather than to give warning that the structure or component is no longer reliable. Incorporated with life extension logic, the concept of probabilistic modeling has been proven and implemented in a NASA proprietary Honeywell TFE731 engine.
Life-Extending Control for Aircraft Engines
The purpose of life-extending-control (LEC) is to study the relationship between control action and engine component life usage, and to design an intelligent control algorithm to provide proper trade-offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating costs. The objective of the LEC for aeropropulsion engines is to maintain the desired performance and operability while reducing component damage. To meet this challenge, the LEC logic is to preferably trim the standard engine control logic with a limited authority. We are studying the potential benefit of an intelligent LEC logic as it applies to commercial gas turbine engines and demonstrates how an intelligent engine control algorithm can drastically reduce the thermal mechanical fatigue damage of a commercial aircraft engine;s high pressure turbine's first stage cooled stator.
Selected Publications:
Y. Xiao, H. Chen, B. Sun, and C. L. P. Chen, "Optimal Utilization in Mobile Database Failure Restoration," IEEE Trans. on Wireless Communications, Vol. 6, No. 7, 2007.
C. L. P. Chen and T-H Guo, "Design of Intelligent Optimal Acceleration Schedules for Extending Life of Aircraft Engines", IEEE Trans. on Systems, Man, and Cybernetics, Part C, 2007 (in press).
Y. Xiao, H. Li, C. L. P. Chen, B. Wang, and Y. Pan, "Proportional Degradation Services in Wireless/Mobile Adaptive Multimedia Networks," Journal of Wireless Communications and Mobile Computing, John Wiley & Sons, Vol. 5, No. 2, Feb. 2005, pp. 219 - 243.
B. Wang, X. Su, and C. L. P. Chen, A Bandwidth Guaranteed Integrated Routing Algorithm in IP over WDM Optical Networks, Photonic Network Communications, Vol. 5, No. 3, May, 2003, pp. 227-247.
T-H Guo, C. L. Philip Chen, Duane L. Mattern, and Link C. Jaw Model-Based Sensor Validation for a Turbofan Engine using Auto-Associative Neural Networks, Journal of Smart Engineering Systems, Vol. 5, 2003, pp. 21-32, Taylor and Francis Press.
Nagatomi, J., B.P. Arulanandam. D.W. Metzger, A. Meunier, R. Bizios. Effects of cyclic pressure on bone marrow cell cultures. J Biomechanical Engineer 124:308-314, 2002.
Ping Guo and C. L. Philip Chen, Regularization Parameter Estimation Based on B Bayesian-Kullback Data Smoothing Theory for Feedforward Neural Networks, IEEE Trans. on Systems, Man, and Cybernetics, Part B, Vol. 33, No. 1, 2003, pp. 35-44.
Contact Information:
Department of Electrical Engineering, Room BSE 1.510
University of Texas at San Antonio
Phone: 210-458-7076
Fax: 210-458-5947
Philip.Chen@utsa.edu
Webpage: http://www.eng.utsa.edu/~pchen
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