Predictive maintenance (PdM) techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach claims more 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 appeal of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. By taking into account measurements of the state of the equipment, 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 does take into account the current condition of equipment (with measurements), instead of average or expected life statistics, to predict when maintenance will be required. Machine Learning approaches are adopted for the forecasting of its future states. 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, and maintenance scheduling and resource optimization. Predictive maintenance has been considered one of the driving forces for improving productivity and a way to achieve just-in-time manufacturing.
Overview 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 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 therefore is not cost-effective. 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. In today's dynamic landscape of service maintenance, prolonged repair processes present a significant challenge for organizations striving to maintain operational excellence. Extended downtime, increased Mean Time to Repair (MTTR), and production losses not only affect profitability but also disrupt service continuity and diminish customer satisfaction. As equipment ages and maintenance requirements intensify, the quest for innovative solutions becomes increasingly urgent. 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. Unless this is achieved, the predictive maintenance solution is of limited value, at least if the solution is implemented on a medium to large size plant with tens of thousands pieces of equipment. In 2010, the mining company Boliden, implemented a combined Distributed Control System and predictive maintenance solution integrated with the plant computerized maintenance management system on an object to object level, transferring equipment data using protocols like Highway Addressable Remote Transducer Protocol, IEC61850 and OLE for process control.
Technologies To evaluate equipment condition, predictive maintenance utilizes nondestructive testing technologies such as infrared, acoustic (partial discharge and airborne ultrasonic), corona detection, vibration analysis, sound level measurements, oil analysis, and other specific online tests. A new approach in this area is to utilize measurements on the actual equipment in combination with measurement of process performance, measured by other devices, to trigger equipment maintenance. This is primarily available in collaborative process automation systems (CPAS). Site measurements are often supported by wireless sensor networks to reduce the wiring cost.
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