What You Should Take Away

Production companies are now capable of keeping their critical assets in peak or near-peak running conditions and increase scheduled asset availability to approach 100% uptime, using the EIDI effectively, through methods described in this chapter. Companies will be able to minimize unscheduled downtimes, avoid lost production, and significantly reduce parts and labor costs for unscheduled equipment repairs. Once a company has implemented a reliable data-driven CBM program, predictive analytic tools can take this a step further by determining when assets are predicted to fail. This knowledge provides companies the ability to more effectively budget for new equipment, schedule site outages, and conduct repairs.

This chapter also provides an evolution of plant controls and how companies are using more business information as part of their control strategy. As we peer into the future, it is likely that the next generation of plant/ refinery control systems include software components such as ML, statistical modeling, and predictive features.

Because experts can be located anywhere, the maintenance notification system is capable of sending messages to a variety of destinations such as email or phone texts. Notifications provide the recipient with the ability to acknowledge the alert directly from the email message or remote device. All notification and acknowledgment activity is logged by the notification server and is made visible to everyone involved in the maintenance process so that they can effectively cooperate to solve the problem. Escalation functionality is used when a notification is sent and the EIDI does not receive an acknowledgment within a prescribed time (Bascur et al. 2016).

In addition, the team has followed best practices by incorporating maintenance of their sensors, controllers, and associated equipment, as these also degrade and require special attention to avoid costly hidden losses. Figure 6.15 represents an advanced asset management system deployment, with automated inputs, online analytics, and connectivity to work management systems and asset data bases. Often there is a dedicated group of subject matter experts within the enterprise who initially get this data to interpret whether issues are urgent, or can be scheduled for the next maintenance event. This approach allows production and operations people to focus on meeting production targets without having to decide whether to shut down equipment during product runs. These multiple groups coordinate to determine the best course of action when situations arise.

FIGURE 6.15

Integrated asset health management system approach.

For a complete guidebook on asset management, please click the link to OSIsoft's condition-based maintenance best practices document at www.osisoft.com/predictivemaintenance.

 
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