Tamer Turna

Abstract:

Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or preferably continuous (online) equipment condition monitoring. The ultimate goal of the approach is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold. This results in a reduction in unplanned downtime costs because of failure where for instance costs can be in the hundreds of thousands per day depending on industry. In energy production in addition to loss of revenue and component costs, fines can be levied for none delivery increasing costs even further. This is in contrast to time- and/or operation count-based maintenance, where a piece of equipment gets maintained whether it needs it or not. Time-based maintenance is labor intensive, ineffective in identifying problems that develop between scheduled inspections, and so is not cost-effective. The fundamental idea is to transform the traditional ‘fail and fix’ maintenance practice to a ‘predict and prevent’ approach.
The "predictive" component of predictive maintenance stems from the goal of predicting the future trend of the equipment's condition. This approach uses principles of statistical process control to determine at what point in the future maintenance activities will be appropriate.
Most predictive inspections are performed while equipment is in service, thereby minimizing disruption of normal system operations. Adoption of predictive maintenance can result in substantial cost savings and higher system reliability.
Reliability-centered maintenance emphasizes the use of predictive maintenance techniques in addition to traditional preventive measures. When properly implemented, it provides companies with a tool for achieving lowest asset net present costs for a given level of performance and risk.
One goal is to transfer the predictive maintenance data to a computerized maintenance management system so that the equipment condition data is sent to the right equipment object to trigger maintenance planning, work order execution, and reporting. By doing so the OPEX and CAPEX saving feature of predictive maintenance solution value is accelerated.
This paper and its attachments provide an insight of how the products of as an example cmc Instruments GmbH and others, help users to achieve their goals in setting up real and beneficiary PREDICTIVE MAINTENANCE MANAGEMENT SYSTEMS.