In recent years, predictive maintenance strategies has been widely discussed among asset intensive organizations. Now more than ever, continuous production and equipment uptime is critical for an organization’s overall success. As a result, there has been a growing trend towards predictive maintenance strategies as a means to prevent disruptions in equipment uptime.
With the rise in this trend, comes a need for organizations to integrate predictive maintenance into their currently existing procedures and systems, including their Enterprise Asset Management (EAM) applications.
At present, EAM applications collect maintenance history on assets which can be analyzed for historical trends. These trends can then be used to predict when a future failure is likely to occur. As a result, preventive inspections or proactive replacements can be scheduled to prevent the anticipated failures.
Accessing real-time operational information (i.e. temperatures, pressures, vibration readings, etc.) can enable predictive analysis and, when integrated with your eAM application, can notify Maintenance Personnel when suspect readings are found.
How do industry leaders penetrate the sometimes-complex predictive maintenance domain? One entry level integration to operational systems is the collection of asset meter readings. Preventive Maintenance work orders can then be more accurately scheduled based on these actual readings.
An example of a more advanced integration is tracking equipment downtime and predicting downtime events. Equipment downtime that leads to production loss is a key performance indicator used by many companies. Management asks, ‘Are production losses caused by operational downtime or downtime related to asset maintenance?’. This question provides many hours of lively conversation in morning production meetings around the globe. Industry leaders recognize the positive impact of sharing this information with the Maintenance organization. Benefits are magnified if the information is being shared in real-time.
Industry leaders utilize EAM Applications that receive information from operational systems to notify maintenance technicians and operations personnel that a maintenance issue may be imminent. At the same time the EAM Application creates the maintenance work order, with the supporting documentation required to mobilize personnel and materials to address the predicted issue. An important design feature of the operational integration with EAM is to not only identify issues but also to filter out the equipment downtime conditions that do not require maintenance personnel.
Overall, current EAM applications can receive and act on predictive data. With the onset of IIoT strategies and products, we now have a means to enable collection of conditional predictive data and utilize the established integration with EAM applications to enable enterprise-wide sharing of this high-value information. Leading businesses will embrace predictive data management and share the information in real-time with their Maintenance Organization.