Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets |
| |
Authors: | Tung-Hsu Hou Wang-Lin Liu Li Lin |
| |
Affiliation: | (1) Institute of Industrial Engineering and Management, National Yunlin University of Science & Technology, Taiwan, ROC;(2) Department of Industrial Engineering, University at Buffalo, State University of New York, USA |
| |
Abstract: | This research develops a methodology for the intelligent remote monitoring and diagnosis of manufacturing processes. A back propagation neural network monitors a manufacturing process and identifies faulty quality categories of the products being produced. For diagnosis of the process, rough set is used to extract the causal relationship between manufacturing parameters and product quality measures. Therefore, an integration of neural networks and a rough set approach not only provides information about what is expected to happen, but also reveals why this has occurred and how to recover from the abnormal condition with specific guidelines on process parameter settings. The methodology is successfully implemented in an Ethernet network environment with sensors and PLC connected to the manufacturing processes and control computers. In an application to a manufacturing system that makes conveyor belts, the back propagation neural network accurately classified quality faults, such as wrinkles and uneven thickness. The rough set also determined the causal relationships between manufacturing parameters, e.g., process temperature, and output quality measures. In addition, rough set provided operating guidelines on specific settings of process parameters to the operators to correct the detected quality problems. The successful implementation of the developed methodology also lays a solid foundation for the development of Internet-based e-manufacturing. |
| |
Keywords: | Remote monitoring computer networks neural networks data mining rough set |
本文献已被 SpringerLink 等数据库收录! |
|