Predictive maintenance helps companies to optimize and streamline maintenance routines and prevent unexpected equipment failures. Predictive analytics often highlights machines or parts that require constant attention, which lets factory technicians adjust their stock of tools and spare parts as necessary. 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.
This approach also 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.
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