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Experience Based Diagnostics and Condition Based Maintenance Within Production Systems
Publication Type:
Conference/Workshop Paper
Venue:
COMADEM 2005, The 18th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management
Abstract
Production efficiency within the manufacturing industry averages on 60% and there is large consensus that 80% is a realistic goal. This makes loss of production capacity the single largest loss in a products life cycle costs [1]. Reducing down time within production systems is therefore essential for increased profit. Hence, experience and knowledge on how downtime in production is reduced is one of the most valuable assets in company. Maintenance and down time are directly related. Easy accessible experience and knowledge on how to determine, and then perform preventive or corrective maintenance is essential for a maintenance engineer. Experience and knowledge is directly linked to the down time of the equipment. An issue is addressed by collecting and structuring experience in symptom, diagnosis & case study solutions. Both human experience and automatically collected experience is captured and reused automatically and semi-automatically. This is achieved using decision support systems based on methods and techniques from e.g. artificial intelligence, knowledge discovery and case-based reasoning. In one application experienced operators are able to classify faults by listening to the industrial equipment has been performed [2]. Only the most experienced operators are able to make reliable diagnosis based on sounds. When the expert retired, a case-based system that classifies the fault based on a sound recording was developed. The system generates a list with the most similar cases including symptoms, diagnosis and information on corrective actions. Experience cases may be collected world wide from similar machines improving the performance of the maintenance system. If experience is stored using industry standard [3], a global Internet search for matching cases is possible. The approach will considerably reduce fault identification time and fault correction time and reduce down time. This paper will discuss and describe experience-based diagnostics and condition based maintenance within production systems and applications within robotic cells.
Bibtex
@inproceedings{Funk811,
author = {Peter Funk and Mats Jackson},
title = {Experience Based Diagnostics and Condition Based Maintenance Within Production Systems},
editor = {David Mba},
month = {August},
year = {2005},
booktitle = {COMADEM 2005, The 18th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management},
url = {http://www.es.mdu.se/publications/811-}
}
