Department of Mechanical and Aerospace Engineering

ROBERT X. GAO

 


Cady Staley Professor of Engineering 
Department Chair, Mechanical & Aerospace Engineering

Contact Information:

Office Location: Glennan 479C
Phone Number: 216-368-6045
Email: Robert.Gao@case.edu

Education

PhD, Mechanical Engineering, Technical University of Berlin, Germany, 1991
MS, Mechanical Engineering, Technical University of Berlin, Germany, 1985
BS, Central Academy of Arts and Design, Beijing, China, 1982

Honors and Awards

·      Elected Fellow:

-    American Society of Mechanical Engineers (ASME)
-    Institute of Electrical and Electronics Engineers (IEEE)
-    Society of Manufacturing Engineers (SME, 2014)
-    The International Academy for Production Engineering (CIRP)     

·      Elected Member: Connecticut Academy of Science and Engineering

·      Pratt & Whitney Chair Professorship, University of Connecticut

·      Distinguished Lecturer: IEEE Instrumentation and Measurement Society; IEEE Electron            Devices Society

·      Technical Award: IEEE Instrumentation and Measurement Society

·      Outstanding Associate Editor Award: IEEE Transactions on Instrumentation and                        Measurement

·      Best Paper/Best Student Paper Awards

·      Outstanding Junior and Senior Faculty Awards

·      Research Excellence Award

·      NSF CAREER Award

Research Interest

Robert Gao’s research interest is in the areas of physics-based and process-embedded sensing methodologies, design, modeling, and characterization of measurement instruments, acoustics and vibration, multi-resolution signal processing, machine learning techniques, stochastic modeling, and RF and acoustic-based wireless data transmission. The goal of his research is to improve the observability in cyber physical systems for improved process, equipment, and product quality control, failure root cause diagnosis, and remaining useful life (RUL) prognosis. Applications include manufacturing, aircraft engines, building HVAC systems, etc. 

Select Publications
Books and Book Chapters

·      R. Gao and R. Yan, “Wavelet: Theory and Application for Manufacturing”, Springer, New York, Dordrecht, Heidelberg, London, ISBN 978-1-4419-1544-3, January, 2011.

·      L. Wang and R. Gao (Eds.), “Condition Monitoring and Control for Intelligent Manufacturing”, Springer, UK, ISBN 1-84628-268-3, May, 2006.

·      R. Gao and P. Wang, “Sensors to Control Processing and Improve Lifetime and Performance for Sustainable Manufacturing”, in Encyclopedia of Sustainable Technologies, Elsevier, (Ed. M. Abraham), pp. 447-462, DOI: 10.1016/B978-0-12-409548-9.10217-9, May, 2017.

·      S. Liu and R. Gao, “Multisensor Data Fusion: Architecture Design and Application in Physical Activity Assessment”, in Multisensor Data Fusion: From Algorithm and Architecture design to Applications (Eds. H. Fourati and K. Iniewski), CRC Press, March, 2015.

·      Z. Fan, R. Gao, and J. Wang, “Virtual Instrumentation for Electrical Capacitance Tomography”, in LabView: Practical Applications and Solutions, InTech, ISBN 978-953-307-650-8, 2011.

·      D. Ball, R. Yan, R. Gao, and A. Deshmukh, “Inferencing in Large Scale Sensor Networks”, in Recent Advances in Maintenance and Infrastructure Management (Eds. R. Cigolini, A. Deshmukh, L. Fedele, and S. McComb), Springer Verlag, ISBN 1-84882-488-1, March, 2009.

·      R. Gao and S. Sheng, “Non-Destructive Testing for Bearing Condition Monitoring and Health Diagnosis”, in Ultrasonic and Advanced Methods for Nondestructive Testing and Material Characterization (Ed. C.H. Chen), World Scientific Publishing, pp. 439-470, ISBN-13 978-981-270-409-2, June, 2007.

·      R. Gao, R. Yan, S. Sheng, and L. Zhang, “Sensor Placement and Signal Processing for Bearing Condition Monitoring”, in Condition Monitoring and Control for Intelligent Manufacturing, Springer Verlag (Eds. L. Wang and R. Gao), pp. 167-191, UK, 2006.

·      R. Gao, “Neural Networks for Machine Condition Monitoring and Fault Diagnosis”, in Neural Networks for Instrumentation, Measurement, and Related Industrial Applications (Eds. S. Ablameyko et al., ISBN 1387-6694), IOS Press, pp. 167-188, Amsterdam, The Netherlands, 2003.

Recent Journal Papers

·      P. Wang, Z. Fan, D. Kazmer, and R. Gao, “Orthogonal analysis of multi-sensor data fusion for improved quality control”, ASME Journal of Manufacturing Science and Engineering, Vol. 139, 101008-1–8, 2017.

·      P. Wang, R. Gao, and R. Yan, “A deep learning-based approach to material removal rate prediction in polishing”, CIRP Annals-Manufacturing Systems, Vol. 66, No. 1, pp. 429-432, 2017.

·      P. Wang, Ananya, R. Yan, and R. Gao, “Visualization and deep recognition for system fault classification”, Journal of Manufacturing Systems, Vol. 44, pp. 310-316, 2017.

·      J. Wang, Y. Zheng, P. Wang, and R. Gao, “A virtual sensing based augmented particle filtering for tool condition prognosis”, Journal of Manufacturing Processes, Vol. 28, pp. 472-478, 2017.

·      P. Wang and R. Gao, “Automated performance tracking for heat exchangers in HVAC”, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 2, pp. 634-645, 2017.

·      J. Wang, L. Zhang, L. Duan, and R. Gao, “A new paradigm of cloud-based predictive maintenance for intelligent manufacturing”, Journal of Intelligent Manufacturing, Vol. 28, No. 5, pp. 1125-1137, 2017.

·      G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “Validation of an in-mold multivariate sensor for measurement of melt temperature, pressure, velocity, and viscosity”, International Polymer Processing, Vol. 32, No. 4, pp. 406-415, 2017.

·      D. Wu, C. Jennings, J. Terpenny, R. Gao, and S. Kumara, “A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forest”, ASME Journal of Manufacturing Science and Engineering, Vol. 139, No. 7, pp. 0710-0718, 2017.

·      D. Wu, S. Liu, L. Zhang, J. Terpenny, R. Gao, T. Kurfess, and J. Guzzo, “A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing”, Journal of Manufacturing Science and Engineering, Vol. 43, pp. 25-34, 2017.

·      Y. Qian, R. Yan, and R. Gao, “A multi-time scale approach to remaining useful life prediction in rolling bearing”, Mechanical Systems and Signal Processing, Vol. 83, pp. 549-567, 2017.

·      P. Wang and R. Gao, “Markov nonlinear system estimation for engine performance tracking”, ASME Journal Engineering for Gas Turbine and Power, Vol. 138, No. 9, pp. 091201, 2016.

·      X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun, and Z. Yang, “Analysis of laminated plates and shells using B-Spline wavelet on interval finite element”, International Journal of Structural Stability and Dynamics, DOI: 10.1142/S0219455417500626, 2016.

·      G. Mendible, D. Kazmer, R. Gao, and S. Johnston, “Estimation of bulk melt-temperature from in-mold thermal sensors for injection molding, Part B: Validation”, International Polymer Processing, Vol. 31, No. 3, pp. 278-284, 2016.

·      W. Wen, R. Gao, and W. Cheng, “Planetary gearbox fault diagnosis using envelop manifold demodulation”, Shock and Vibration, Vol. 2016, paper # 3952325, pp. 1-13, 2016.

·      X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun and Z. Yang, “Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization”, Journal of Sound and Vibration, Vol. 375, No. 4, pp. 200-216, 2016.

·      S. Sah, N. Mahayotsanun, M. Peshkin, J. Cao, and R. Gao, “Pressure and draw-in maps for stamping process monitoring”, ASME Journal of Manufacturing Science and Engineering, Vol. 138, No. 9, 2016.

·      X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun and Z. Yang, “Construction and application of multivariable wavelet finite element for flat shell analysis”, Computer Modeling in Engineering and Sciences, 2016.

·      X. Zhang, C. Wang, R. Gao, R. Yan, X. Chen and S. Wang, ”A novel hybrid error criterion-based active control method for on-line milling vibration suppression with piezoelectric actuators and sensors”, Sensors, Vol. 16, Issue 1, paper #68, 2016.

·      X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun and Z. Yang, “B-spline wavelet on interval finite element method for static and vibration analysis of stiffened flexible thin plate”, Computers, Materials & Continua, Vol. 52, No. 1, pp. 53-71, 2016.

·      X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun, Z. Yang, ”Analysis of laminated and shells using B-spline wavelet on interval finite element”, International Journal of Structural and Dynamics, paper #1750062, 2016.

·      P. Wang, R. Gao, and Z. Fan, “Cloud Computing for Manufacturing: Benefits and Limitations”, ASME Journal of Manufacturing Science and Engineering, Vol. 137, No. 4, pp. 040901, 2015.

·      P. Wang, D. Karg, R. Gao, Z. Fan, K. Kwolek, and A. Consiglio, “Non-Contact Identification of Rotating Blade Vibration”, JSME Mechanical Engineering Journal, Vol. 2, No. 3, pp. 1-12, 2015.

·      P. Wang and R. Gao, “Adaptive Resampling-Based Particle Filtering For Tool Life Prediction”, Journal of Manufacturing Systems, Vol. 37, No. 2, pp. 528-534, 2015.

·      J. Wang, P. Wang, and R. Gao, “Enhanced Particle Filter for Tool Wear Prediction”, Journal of Manufacturing Systems, Vol. 36, pp. 35-45, 2015.

·      S. Johnston, G. Mendible, R. X. Gao, and D. Kazmer, “Estimation of bulk melt-temperature from in-mold thermal sensors for injection molding, Part A: Method”, International Polymer Processing, Vol. 30, No. 4, pp. 460-466, 2015.

·      P. Wang and R. Gao, “Adaptive resampling-based particle filtering for tool life prediction”, Journal of Manufacturing Process, Vol. 37, No. 2, pp. 528-534, 2015. 

·      R. Yan, R. Gao and L. Zhang, “In-process modal parameter identification for spindle health monitoring”, Mechatronics, Vol. 31, pp. 42-49, 2015.

·      R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enabled prognosis for manufacturing”, CIRP Annals – Manufacturing Technology, Vol. 64 No. 2, pp. 749-772, 2015.

·      P. Wang, R. Gao, and Z. Fan, “Cloud computing for cloud manufacturing: benefits and limitations”, ASME Journal of Manufacturing Science and Engineering, Vol. 137, 040901-1-9, 2015.

·      G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “In-mold multivariate sensing of colored polystyrene”, Polymer Engineering and Science, Vol 55, No. 12, pp. 2794-2800, 2015.

·      J. Wang, P. Wang, and R. Gao, “Enhanced particle filter for tool wear prediction”, Journal of Manufacturing Systems, Vol. 36, pp. 35-45, 2015.

·      P. Wang, D. Karg, Z. Fan, R. Gao, K. Kwolek, and A. Consiglio, “Non-contact identification of rotating blade vibration”, JSME Mechanical Engineering Journal, Japan Society of Mechanical Engineering, Vol. 2, No. 3, pp. 1-12, 2015.

·      M. Ng, L. Li, Z. Fan, R. Gao, E. Smith, K. Ehmann, and J. Cao, “Joining sheet metals by electrically-assisted roll bonding”, CIRP Annals – Manufacturing Technology, Vol. 64, pp. 273-276, 2015.

·      Z. Fan, X. Zou, R. Gao, M. Ng, J. Cao, and E. Smith, “Embedded capacitive pressure sensing for electrically assisted microrolling”, IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 3, pp. 1005-1014, 2015.

·      D. Kazmer, G. Gordon, G. Mendible, S. Johnston, X. Tang, Z. Fan, and R. Gao, “A multivariate sensor for intelligent polymer processing”, IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 3, pp. 1015-1023, 2015.

·      G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “Quality control using a multivariate injection molding sensor”, International Journal of Advanced Manufacturing Technology, Vol. 78, pp. 1381-1391, June, 2015.

·      X. Zou, Z. Fan, R. Gao, and J. Cao, “An integrative approach to spatial mapping of pressure distribution in microrolling”, CIRP Journal of Manufacturing Science and Technology, Vol. 9, pp. 107-115, 2015.

·      R. Gao, X. Tang, G. Gordon, and D. Kazmer, “Online product quality monitoring through in-process measurement”, CIRP Annals – Manufacturing Technology, Vol. 63(1), pp. 493-496, 2014.

·      M. Ng, Z. Fan, R. Gao, E. Smith, and J. Cao, “Characterization of electrically-assisted micro-rolling for surface texturing using embedded sensor”, CIRP Annals – Manufacturing Technology, Vol. 63(1), pp. 269-272, 2014.

·      W. Cheng, R. Gao, J. Wang, T. Wang, W. Wen, and J. Li, “Envelope deformation in computed order tracking and error in order analysis”, Mechanical Systems and Signal Processing, Vol. 48, No. 1, pp. 92-102, 2014.

·      W. Cheng, T. Wang, W. Wen, J. Li, and R. Gao, “A new feature selection algorithm based on the mean impact variance”, Mathematical Problems in Engineering, pp. 1-8, 2014.

·      W. Xiang, S. Yan, J. Wu, and R. Gao, “Complexity evaluation on non-linear dynamic behavior of mechanisms with clearance joints by using the fractal method”, Journal of Mechanical Engineering Science, Proceedings of the Institution of Mechanical Engineers, Part C, 2014.

·      D. Cui, S. Yan, X. Guo, and R. Gao, “Parametric resonance of liquid sloshing in partially filled spacecraft tanks during the powered-flight phase of rocket”, Aerospace Science and Technology, Vol. 35, pp. 93-105, 2014.

·      J. Wu, S. Yan, and R. Gao, “Modeling and Analysis of Failure Propagation of Mechanical System with Multi-Operation States Using High-level Petri Net”, Journal of Risk and Reliability, Proceedings of the Institution of Mechanical Engineers, Part O, Vol. 228, pp. 347-361, 2014.

·      R. Yan, Y. Qian, S. Hu, and R. Gao, “Wind turbine gearbox fault diagnosis based on wavelet domain stationary subspace analysis”, Chinese Journal of Mechanical Engineering, Vol. 50, No. 11, pp. 9-16, 2014.

·      J. Wang, R. Gao, and R. Yan, “Integration of EEMD and ICA for wind turbine gearbox diagnosis”, Wind Energy, Vol. 17, No. 5, pp. 757-733, 2014.

·      J. Wang, R. Gao, and R. Yan, “Multi-scale enveloping order spectrogram for rotating machine health diagnosis”, Mechanical Systems and Signal Processing, Vol. 46, pp. 28-44, 2014.