Knowledge based systems for machine health monitoring
2013 (English)In: 26th International Congress on Condition Monitoring and Diagnostic Engineering (COMADEM 2013): Helsinki, Finland, June 11 - 13, 2013 / [ed] Raj Rao B.K.N., 2013, 1-8 p.Conference paper (Refereed)
The advances in manufacturing technology and the competition in the market necessitate the continuous availability of machinery for production. This has created a need for effective and efficient maintenance practices resulting in improved plant performance. Instead of the common fragmented approaches to maintenance, an integrated approach is recommended. One such approach to effective maintenance is, Knowledge Based Maintenance Systems for machine health monitoring which can be integrated with plant maintenance. A fault diagnosis system with Knowledge Based System (KBS) is based on computer programs interlinking fault symptoms, faults and remedies. A comprehensive KBS system can be developed for industrial machinery which can monitor the major machinery faults or abnormalities of the machining centres and provide expert maintenance solutions through measurement and analysis of machine health parameters such as vibration, temperature, wear debris, lubricant condition, etc. These solutions are based on published information about permissible machine parameters in handbooks, journals and conferences, past maintenance experiences and from machine expert’s knowledge regarding specific machinery problem and its solution. The objective of the present research paper is, to suggest one such system for machine health monitoring which would be a useful maintenance tool in a majority of small and medium scale manufacturing units. The KBS for machine health monitoring has modules on various aspects of machine health monitoring. Many of these sections are interactive type with easy-to-answer questions, posed by the system. Based on the user’s response and built-in knowledge base, comparisons are made by the system and appropriate maintenance solutions are suggested to the used.
Place, publisher, year, edition, pages
2013. 1-8 p.
Research subject Operation and Maintenance
IdentifiersURN: urn:nbn:se:ltu:diva-34935Local ID: 93e4d919-aef0-4b46-a5de-9f3c3d5d2674OAI: oai:DiVA.org:ltu-34935DiVA: diva2:1008187
International Congress of Condition Monitoring and Diagnostic Engineering Management : 11/06/2013 - 13/06/2013
Godkänd; 2013; 20130702 (mahnad)2016-09-302016-09-30Bibliographically approved