Alexander Parlos
Professor
Office: 116 Engineering/Physics Building Office Wing
Phone: 979-862-2060 Fax: 979-845-3081
Email: aparlos@tamu.edu
Personal Web Page
INTEREST AREAS
• Non-intrusive and Non-invasive Methods for Condition Monitoring, Assessment and End-of-Life Prediction.
• Non-intrusive and Non-invasive Methods for Energy Conversion Efficiency Assessment.
• Distributed Sensor and Actuator Networks for Condition and Performance Monitoring.
• Machine Learning and Pattern Recognition for Dynamic Estimation and Control.
• Intelligent Control for Life Extension.
• Adaptive Flow Control for Distributed Real-time Applications.
EDUCATION
• Sc.D., Automatic Control & Systems Engineering, MIT, 1986.
• S.M., Mechanical Engineering, MIT, 1985.
• S.M., Nuclear Engineering, MIT, 1985.
• B.S., Nuclear Engineering, Texas A&M University, 1983.
EXPERIENCE
Dr. Parlos is a Professor of Mechanical Engineering at Texas A&M University, with joint appointments in the Department of Nuclear Engineering, and, by courtesy, Department of Electrical and Computer Engineering. He has established and is heading the Networked and Intelligent Machines Laboratory (NIML) where graduate and undergraduate students, and postdoctoral visitors conduct research on non-invasive and non-intrusive interfaces for machine monitoring and related applications utilizing standard IP networks for communication, and on adaptive methods rooted in machine learning, pattern recognition and more traditional control engineering approaches. His applied research interests include the development of data-driven approaches for condition and performance assessment of various dynamic systems. He has been involved with the particular application of these concepts to electro-mechanical and mechanical systems, and more recently to distributed real-time computer systems. His theoretical research interests involve the development of learning algorithms for neural networks and their use in nonlinear estimation and predictive control. Since his arrival to Texas A&M University he has supervised the research of over 50 graduate students. His research is sponsored by various federal agencies (NSF, US Department of Energy, US Department of Defense, NASA), State of Texas agencies, industry groups (EPRI, APPA), as well as private companies. Dr. Parlos serves on several Editorial Boards of research journals and on various national and international technical committees and review panels for federal funding agencies. He is a Fellow of the ASME and a licensed professional engineer in The State of Texas.
RESEARCH
My current theoretical research activities involve the development of adaptive machine learning algorithms for use in various nonlinear estimation and control problems encountered in the study of dynamic systems. Many of the algorithms my group develops are intended for use with recurrent and feedforward neural networks. Currently, my applied research interests are concentrated on the application of statistical signal processing and control algorithms in two areas: (a) the assessment of machine system condition and performance, and the related areas of end-of-life prediction and life-extending controls, and, (b) estimation and control problems related to (i) inelastic (or real-time) network traffic, and (ii) distributed software systems. More details about my current research activities can be found on my personal web site.
HONORS AND AWARDS
• ASME Fellow, 2007
• Alcoa Fellow, 2006
• TEES Fellow, 2005
• TEES Fellow, 2003
• NASA Tech Brief Invention Award “A Dynamic Learning Algorithm for the Recurrent Multilayer Perceptron Neural Network with Global Feedback,” 1999
• NASA Tech Brief Invention Award “Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads,” 1999
• NASA Tech Brief Invention Award “A Method for Adaptive Filtering in Complex Systems Using Recurrent Neural Networks,” 1999
• Texas Engineering Experiment Station (TEES) Select Young Faculty Award, Texas A&M University, September 1990
• Member, Tau Beta Pi, National Engineering Honor Society
• Member, Phi Kappa Phi, National Honor Society
• Member, Alpha Niu Sigma, National Nuclear Engineering Honor Society
PATENTS
• “Neural Node, Network and Model, and Method of Teaching the Same,” U.S. Patent No. 5,479,571, December 26, 1995 (pdf).
• “Method and System for Early Detection of Incipient Faults in Electric Motors,” U.S. Patent No. 6,590,362, July 8, 2003 (pdf).
• “Method and System for Determining Induction Motor Speed,” U.S. Patent No. 6,713,978, April 15, 2004 (pdf).
• “Method and System for Training a Recurrent Network,” U.S. Patent No. 6,963,862, November 8, 2005 (pdf).
• “Condition Assessment and Life Expectancy Prediction for Devices,” U.S. Patent No. 7,024,335, April 4, 2006 (pdf).
SELECT RECENT PUBLICATIONS
• Parlos, A. G., Menon, S. K., and Atiya, A. F., “An algorithmic approach to adaptive state filtering using recurrent neural networks,” IEEE Transactions on Neural Networks, Vol. 12, No. 6, pp. 1411-1432, November 2001 (pdf).
• Parlos, A. G., Menon, S. K., and Atiya, A. F., "An Adaptive State Filtering Algorithm for Systems with Partially Known Dynamics," Journal of Dynamic Systems, Measurement and Control, Vol. 124, No. 3, pp. 364-374, September 2002 (pdf).
• Kim, K., and A. G. Parlos, “Induction Motor Fault Diagnosis Based on Neuro-predictors and Wavelet Signal Processing,” IEEE/ASME Transactions on Mechatronics, Vol. 7, No. 2, pp. 201-219, June 2002 (pdf).
• Kim, K., and A. G. Parlos, “Model-Based Fault Diagnosis of Induction Motors Using Non-Stationary Signal Segmentation,” Mechanical Systems and Signal Processing, Vol. 26, No. 2-3, pp. 223-253, 2002 (pdf).
• Bharadwaj, R. M., and A. G. Parlos, “Neural State Filtering for Adaptive Induction Motor Speed Estimation,” Mechanical Systems and Signal Processing, Vol. 17, No. 5, pp. 903-924, 2003 (pdf).
• Kim. K., and A. G. Parlos, “Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis,” Journal of Dynamic Systems, Measurement and Control, Vol. 125, No. 1, pp. 80-95, 2003 (pdf).
• Kim. K., A. G. Parlos, and R. M. Bharadwaj, “Sensorless Fault Diagnosis of Induction Motors,” IEEE Transactions on Industrial Electronics, Vol. 50, No. 5, pp. 1038-1051, 2003 (pdf).
• William, R. B., and A. G. Parlos, “Adaptive State Filtering for Space Shuttle Main Engine Turbine Health Monitoring,” Journal of Spacecraft and Rockets, Vol. 40, No.1, pp. 101-109, January-February 2003 (pdf).
• Bhattacharyya, A., A. G. Parlos, and A. F. Atiya, “Prediction of MPEG-coded Video Source Traffic Using Recurrent Neural Networks,” IEEE Transactions on Signal Processing, Special Issue in Networking, Vol. 51, No. 8, pp. 2177-2190, 2003 (pdf).
• Bharadwaj, R. M., A. G. Parlos, “Neural Speed Filtering for Sensorless Induction Motor Drives,” Control Engineering Practice, Vol. 12, pp. 687-706, 2004 (pdf).
• Parlos, A. G., Kim, K. and Bharadwaj, R., “Sensorless Detection of Mechanical Faults in Electromechanical Systems,” Mechatronics, Vol. 13, pp. 357-380, 2004 (pdf).
• Bharadwaj, R. M., and A. G. Parlos, “Neural Speed Filtering for Induction Motors with Anomalies and Incipient Faults,” IEEE/ASME Transactions on Mechatronics, Vol. 9, No. 4, pp. 679-688, December 2004 (pdf).
• Kazantzis, N., Chong, K. T., J. Y. Park and A. G. Parlos, “Control-Relevant Discretization of Nonlinear Systems with Time-Delay Using Taylor-Lie Series,” Journal of Dynamic Systems, Measurement and Control, Vol. 127, pp. 153-159, 2005 (pdf).
• Atiya, A. F., M. Aly, and A. G. Parlos, “Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic,” IEEE Transactions on Neural Networks; Special Issue on Adaptive Learning Systems in Communication Networks, Vol. 15, No. 6, pp. 1136-1146, 2005 (pdf).
• Harihara, P. and A. G. Parlos, “Sensorless Detection of Cavitation in Centrifugal Pumps,” Proceedings of the 2006 ASME International Mechanical Engineering Congress and Exposition, Chicago, IL, November 5-10, 2006 (pdf).
• Ye, D. and A. G. Parlos, “Predictive Path Switching Control for Improving the Quality of Service in Real-time Applications,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Network-Aware Multimedia Processing and Communications, Vol. 1, No. 2, pp. 308-318, August 2007 (pdf).
• Harihara, P. and A. G. Parlos, “Sensorless Detection of Impeller Cracks in Motor Driven Centrifugal Pumps,” Proceedings of the 2008 ASME International Mechanical Engineering Congress and Exposition, Boston, MA, November 2-6, 2008 (pdf).
• Harihara, P. and A. G. Parlos, “Sensorless Detection and Isolation of Faults in Motor-Pump Systems,” Proceedings of the 2008 ASME International Mechanical Engineering Congress and Exposition, Boston, MA, November 2-6, 2008 (pdf).
