Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

[+] Author and Article Information
R. Du

Department of Industrial Engineering, University of Windsor, Windsor, Ontario, N9B 3P4 Canada

M. A. Elbestawi

Department of Mechanical Engineering, McMaster University, Hamilton, Ontario, L8S 4L7 Canada

S. M. Wu

Department of Mechanical Engineering and Applied Mechanics, University of Michigan, Ann Arbor, MI 48105 USA

J. Eng. Ind 117(2), 121-132 (May 01, 1995) (12 pages) doi:10.1115/1.2803286 History: Received July 01, 1992; Revised June 01, 1993; Online January 17, 2008


This paper presents a systematic study of various monitoring methods suitable for automated monitoring of manufacturing processes. In general, monitoring is composed of two phases: learning and classification. In the learning phase, the key issue is to establish the relationship between monitoring indices (selected signature features) and the process conditions. Based on this relationship and the current sensor signals, the process condition is then estimated in the classification phase. The monitoring methods discussed in this paper include pattern recognition, fuzzy systems, decision trees, expert systems and neural networks. A brief review of signal processing techniques commonly used in monitoring, such as statistical analysis, spectral analysis, system modeling, bi-spectral analysis and time-frequency distribution, is also included.

Copyright © 1995 by The American Society of Mechanical Engineers
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