A process monitoring system based on multi-sensor data fusion: An experiment study

Author

Xiang, Qian ; Lu, Zhi-Jun ; Li, Bei-Zhi ; Yang, Jiang-guo

fYear

2012

fDate

14-15 Aug. 2012

Firstpage

35

Lastpage

39

Abstract

Multi-sensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Focusing on the indirect method, an attempt was made to build up a multi-sensor data fusion system to monitor the condition of grinding wheels with force signals and the acoustic emission (AE) signals. An artificial immune algorithm based multi-signals processing method was presented in this paper. The intelligent monitoring system is capable of incremental supervised learning of grinding conditions and quickly pattern recognition, and can continually improve the monitoring precision. The experiment indicates that the accuracy of condition identification is about 87%, and able to meet the industrial need on the whole.

Keywords

acoustic signal processing; artificial immune systems; grinding machines; learning (artificial intelligence); process monitoring; production engineering computing; sensor fusion; acoustic emission signals; artificial immune algorithm; force signals; grinding conditions; grinding wheels; incremental supervised learning; indirect method; multisensor data fusion system; multisignal processing method; process monitoring system; Feature extraction; Immune system; Monitoring; Sensor systems; Wheels; Artificial immune; Grinding; Multi-sensor Data Fusion; Negative-Selection Algorithm; Process Monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on

Conference_Location

Jalarta

Print_ISBN

978-1-4673-1459-6

Type

conf

DOI

10.1109/URKE.2012.6319578

Filename

6319578