Researcher Portfolio

Zhou Junhong (Dr)
Principal Research Engineer II
6510 1626
Manufacturing Execution and Control
Introduction:Dr. Zhou Junhong received the B.Eng. degree from the Automation Department, Tsinghua University and the M.Eng. degree from the Department of Electrical and Computer Engineering, National University of Singapore (NUS), PhD degree from Mechanical and Aerospace Engineering, NTU.  She joined the Singapore Institute of Manufacturing Technology in 1996. She has worked and led for many industrial projects in equipment health monitoring, intelligent condition-based maintenance and process parameters optimization. Her current research interests include autonomous and on-line fault diagnosis and failure prognosis, multimodal sensing and feature extraction, performance and serviceability optimization, artificial intelligence, and statistical analysis. She has been co-authors for 8  journal papers and 32 conference papers.
Research Interest:Autonomous and on-line fault diagnosis and failure prognosis in equipment health monitoring, intelligent condition-based maintenance, multimodal sensing, feature extraction, dominant feature identification, performance and serviceability optimization, artificial intelligence, and statistical analysis.
BioNotes:PhD, Nanyang Technological University, 2012
Master of Engineering, National university of Singapore, 1994
BEng in Automation, Tsinghua University, 1987
Publications:

Geramifard, O.; Xu, J.-X.; Zhou, J.-H.; Li, X.; , "A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics," Industrial Informatics, IEEE Transactions on , vol.8, no.4, pp.964-973, Nov. 2012

 O. Geramifard, J.-X. Xu, and J.-H. Zhou, "A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring", in < Diagnostics and Prognostics of Engineering Systems: Methods and Techniques >, Edited by S. Kadry, IGI Global, 2012.

J . H. Zhou, C. K. Pang, Z. W. Zhong and F. L. Lewis, "Tool Wear Monitoring Using Acoustic Emissions By Dominant Feature Identification", IEEE Transactions on instrumentation and measurement, vol. 60(2), pp. 547-559 (Feb-2011)

X. Li, M. J. Er, B. S. Lim, J. H. Zhou, O. P. Gan and L. Rutkowski, "Fuzzy Regression Modelling for Tool Performance Prediction and Degradation Detection", International Journal of Neural Systems, vol. 20(5), pp. 405-419 (Oct-2010)

J. H. Zhou, C. K. Pang, F. L. Lewis and Z. W. Zhong, "Intelligent Diagnosis and Prognosis of Machine Tool Wear Using Dominant Feature Identification", IEEE Transactions on Industrial Informatics (TII), vol. 5(4), pp. 454-464 (Nov-2009)

X. Li, D. M. Shi, V. Charastrakul and J. H. Zhou, "Advanced P-Tree Based K-Nearest Neighbors for Customer Preference Reasoning Analysis", Journal of Intelligent Manufacturing, vol. Number 5(October), pp. 569-579 (Oct-2009)

J. H. Zhou, Z. W. Zhong, M. Luo and C. Shao, "Wavelet-based correlation modelling for health assessment of fluid dynamic bearings in brushless DC motors", International Journal of Advanced Manufacturing Technology (2008) DOI 10.1007/s00170-008-1508-3. , vol. 41(5-6), pp. 421-429 (May-2009)

J. H. Zhou, L. Wee and Z. W. Zhong, "A Knowledge Base System for Rotary Equipment Fault Detection and Diagnosis", IEEE International Conference on Robotics and Automation, Robotics and Vision (ICARCV, 2010), 12-Dec-2010 to 12-Dec-2010, singapore, vol. 1, pp. 1335-1340

J. H. Zhou and X. Yang, "Reinforced Morlet Wavelet Transform For Bearing Fault Diagnosis", Annual Conference of the IEEE Industrial Electronics Society (IECON), 07-Nov-2010 to 10-Nov-2010, Glendale, AZ, USA, vol. 1, pp. 1173-1178

J. H. Zhou, D. H. Zhang, A. A. Shai, M. Luo and D. W. Wang, "iDiagnosis & Prognosis - An Intelligent Platform for Complex Manufacturing", IEEE Advanced intelligent mechatronics, 14-Jul-2009 to 16-Jul-2009, singapore, vol. WB5.3, pp. 405-410

Awards:2011, Best Application Paper Award in The 8th Asian Control Conference (ASCC 2011), C. K. Pang, J. -H. Zhou, Z. -W. Zhong, and F. L. Lewis, "Industrial Fault Detection and Isolation Using Dominant Feature Identification