You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Case-based reasoning combined with statistics for diagnostics and prognosis

Publication Type:

Conference/Workshop Paper

Venue:

25th International Congress on Condition Monitoring and Diagnostic Engineering

Publisher:

IOPScience


Abstract

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great e ort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the rst step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the nal diagnosis/prognosis step, status and faults are identi ed and classi ed. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

Bibtex

@inproceedings{Olsson2616,
author = {Tomas Olsson and Peter Funk},
title = {Case-based reasoning combined with statistics for diagnostics and prognosis},
editor = {Andrew Ball, Rakesh Mishra, Fengshou Gu and Raj B K N Rao},
pages = {1--9},
month = {June},
year = {2012},
booktitle = {25th International Congress on Condition Monitoring and Diagnostic Engineering},
publisher = {IOPScience},
url = {http://www.es.mdu.se/publications/2616-}
}