Predictive maintenance

Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item. Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item. The main promise of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. The key is 'the right information in the right time'. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people, etc.) and what would have been 'unplanned stops' are transformed to shorter and fewer 'planned stops', thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling. Predictive maintenance differs from preventive maintenance because it relies on the actual condition of equipment, rather than average or expected life statistics, to predict when maintenance will be required. Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, fault detection, time to failure prediction, maintenance scheduling and resource optimization. Predictive maintenance has also been considered to be one of the driving forces for improving productivity and one of the ways to achieve 'just-in-time' in manufacturing. Since 2001, the Center for Intelligent Maintenance Systems Industry/University Collaborative Research Center) has been working in the development of advance methods and technologies for predictive maintenance. The developed approaches have been successfully validated in over 70 projects conducted with research and industry partners for enabling products and systems to achieve and sustain near-zero breakdown. The vision has been to estimate the current health of a plant equipment and predict the next fault event for improved productivity and asset utilization. and later adopted by Fanuc in 2013 Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or 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 non 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