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Proceedings Paper

Robust estimators of redundant sensors for manufacturing quality improvement
Author(s): Yu Ding; Jung Jin Cho; Yong Chen
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Paper Abstract

Recent innovations in sensor technology enable manufacturers to distribute redundant sensors in manufacturing processes for quality monitoring, defect detection, and fault diagnosis. Even if a single sensor is relatively reliable, the large number of sensors in a distributed sensor system confronts us the almost unavoidable possibility that some of the sensors may malfunction. Without isolating sensor anomalies from the underlying process changes, abnormal sensor readings can cause frequent false alarms and jeopardize productivity. Traditionally, sensor system reliability has been ensured by employing off-line gage Repeatability and Reproducibility (R&R) calibration. But this off-line approach can be time consuming and costly for in-process distributed sensor systems. This paper will present a robust estimation procedure that automatically identify the observations related to suspected sensor failures. We first identify sensor redundancy and introduce an existing algorithm to assess the redundant level. We further suggest a decomposition technique, which helps to substantially reduce the computation expense of the existing algorithms for a large sensor system. Finally, the concept and procedure is illustrated using a distributed coordinate sensor system in a multi-station manufacturing system.

Paper Details

Date Published: 16 November 2005
PDF: 12 pages
Proc. SPIE 5999, Intelligent Systems in Design and Manufacturing VI, 599901 (16 November 2005); doi: 10.1117/12.629378
Show Author Affiliations
Yu Ding, Texas A and M Univ. (United States)
Jung Jin Cho, Texas A and M Univ. (United States)
Yong Chen, The Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 5999:
Intelligent Systems in Design and Manufacturing VI
Bhaskaran Gopalakrishnan, Editor(s)

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