Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering. Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.

Dr. Lihui Wang is a research officer of Integrated Manufacturing Technologies Institute at National Research Council of Canada (NRC). He received his Ph.D. and M.Sc. degrees from the Kobe University, Japan in 1993 and 1990, and his B.Sc. from China in 1982, respectively. Prior to joining NRC, he has worked for two years at the Kobe University and another two years at the Toyohashi University of Technology (both in Japan) as an Assistant Professor. His work on web-based monitoring and remote control has won the Best Paper Award at the FAIM 2002 international conference in Germany, and his research on intelligent shop floor has won the Best Poster Award at PRO-VE'03, the 4th IFIP Working Conference on Virtual Enterprises in Switzerland. In addition, he is also a five-time winner of the NRC Institute Awards on Excellence & Leadership in R&D and Global Reach. His research interests are focused on web-based real-time monitoring and control, distributed artificial intelligence, intelligent manufacturing systems, and distributed process planning. He published over 100 research papers in engineering journals and refereed conference proceedings, and has edited 3 conference proceedings on manufacturing research. Dr. Robert X. Gao is an Associate Professor of Mechanical Engineering at the University of Massachusetts Amherst, USA. He received his B.S. degree from China, and his M.S. and Ph.D. from the Technical University Berlin, Germany, in 1982, 1985, and 1991, respectively. Since starting his academic career in 1992, he has been conducting research in the general area of embedded sensors and sensor networks, "smart" electromechanical systems, wireless data communication, and signal processing for machine health monitoring, diagnosis, and prognosis. Dr. Gao has published over 100 refereed papers on journals and international conferences, and has one US patent and two pending patent applications on sensing. He is an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, and served as the Guest Editor for the Special Issue on Sensors of the ASME Journal of Dynamic Systems, Measurement, and Control, published in June, 2004. Condition-based Monitoring and Control for Intelligent Manufacturing has arisen from the Flexible Automation and Intelligent Manufacturing (FAIM 2004) conference, held in Toronto, Canada on July12-14 2004. Thirty papers have been selected out of 170 presented at the conference and the authors of these papers have been invited to submit extended updated versions of these papers in order to create a state of the art review of condition-based monitoring and control in manufacturing.

Monitoring and Control of Machining Precision Manfacturing Process Monitoring with Acoustic Emission Tool Condition Monitoring in Machining Monitoring System for Grinding Processes Condition Monitoring of Rotary Machines Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings Sensor Placement and Signal Processing for Bearing Condition Monitoring Monitoring and Diagnosis of Sheet Metal Stamping Processes Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling Signal Processing in Manufacturing Monitoring Autonomous Active-Sensor Networks for High-Accuracy Monitoring in Manfacturing Remote Monitoring and Control in Distributed Manufacturing Environment An Intelligent Nanofabrication Probe with Function of Surface Displacement/Profile Measurement Smart Transducer Interface Standards for Condition Monitoring and Control of Machines Rocket Testing and Integrated System Health Management