Conference proceeding
Robust estimators of redundant sensors for manufacturing quality improvement
Proceedings of SPIE, Vol.5999(1), pp.599901-5999012
Intelligent Systems in Design and Manufacturing VI
11/09/2005
DOI: 10.1117/12.629378
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.
Details
- Title: Subtitle
- Robust estimators of redundant sensors for manufacturing quality improvement
- Creators
- Yu Ding - Texas A&M UniversityJung Jin Cho - Texas A&M UniversityYong Chen - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of SPIE, Vol.5999(1), pp.599901-5999012
- Conference
- Intelligent Systems in Design and Manufacturing VI
- DOI
- 10.1117/12.629378
- ISSN
- 0277-786X
- Language
- English
- Date published
- 11/09/2005
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984186965602771
Metrics
22 Record Views