Reducing maintenance cost through effective prediction analysis and process integration
Avin Mathew, Sheng Zhang, Lin Ma, Tom Earle, Douglas J. Hargreaves · QUT ePrints (Queensland University of Technology) · 2006
Asset management is playing an ever increasing role in sustaining the competitive capability of many businesses. Maintenance costs form a large proportion of asset management costs, and a reduction in these costs can represent significant savings to the business. Many software vendors provide various asset management solutions but lack complete horizontal and vertical integration; and with the sizeable number of systems come an equal number of software interfaces. With businesses that subscribe to different CMMS and condition monitoring systems from different vendors, system integration becomes a nightmare. By resolving the integration issue and considering holistic information, asset management can ascend to a new level through effective predictive maintenance. This paper outlines a proposal for integrating condition monitoring systems with a CMMS through the use of emerging technologies. The OSA-CBM framework will be implemented to monitor the condition of an asset for health assessment and predictive maintenance scheduling. Data will be stored in a MIMOSA-derived database, with communications between the OSA-CBM system and CMMS using the open standard MIMOSA OSA-EAI. The underlying foundation is a web services platform which will assist in turning asset condition data into real cost savings.