Nikki Bishop, PE and Aaron Crews, Emerson Process Management
It’s no surprise that a good predictive maintenance strategy improves overall reliability and helps to meet production availability targets. However, predictive maintenance based on periodic – possibly infrequent – data acquisition fails to give real-time insight into asset health. Resources are wasted sending skilled personnel for data collection into the field, which might also be a hazardous environment. In fact, studies have shown that more than 60% of a typical instrument technician’s trips to the field result in either no action or a minor configuration adjustment that could be done from their office. So what’s the answer for a good predictive maintenance strategy? Automated monitoring provides online indication of an asset’s health and can detect process conditions that may be unintentionally and unknowingly inducing a fault on equipment, allowing operators to make adjustments so that process related equipment faults can be avoided altogether. With advanced warning, maintenance staff can spend their time servicing assets that actually need it rather than wasting time searching for problems through manual inspection.
While real-time monitoring (and protection) of critical process equipment, such as large compressors or turbines, is standard practice at most facilities, on-line monitoring of second-tier equipment, such as pumps, heat exchangers, blowers, small compressors, pipes/vessels corrosion, cooling towers and air cooled heat exchangers (“fin-fans”) has traditionally been deemed cost-prohibitive or too difficult. Even though these unmonitored or manually monitored assets may not have been originally classified as “critical”, an outage or failure can cause a serious process disturbance or shutdown, resulting in process downtime and increased load on site personnel for repairs. Wireless technology enables additional assets to be monitored by providing an easy, cost-effective means of adding measurements. But as the cost of physically adding the measurements goes down, the monitoring of them still requires some time and attention. The right people and processes need to be in place to ensure that operations and maintenance staff know when there is a problem and know what to do about it. Adding full-time staff to handle these important tasks is typically not an option. But what if you could monitor these essential assets remotely and even have a team of experts ready to diagnose the problem? With remote system monitoring you can do just that, reducing the burden on local resources and supplementing the plant with the people and expertise as they are needed.
The Separations Research Program at the University of Texas-Austin’s J.J Pickle Research Campus has successfully implemented essential asset monitoring strategies for pumps, heat exchangers and blowers. They now have online insight into the health of their assets. They can see when process conditions may be contributing to asset health degradation and make adjustments to prevent further damage or failure. Alerts for increasing vibration warn of impending failures and allow time for servicing before failure occurs. UT has gone one step further by setting up a remote connection where experienced subject matter experts can log in using secure VPN access to help diagnose problems with assets and assist with the appropriate corrective action. Alerts for conditions such as heat exchanger fouling, resonance speed detection, hydrocarbon leaks and pump cavitation can be automatically routed to trained subject matter experts at the onset of the failure condition. The remote monitoring system also monitors for overall system health alerts such as an overloaded PC or failed backup controller. Automated, remote system monitoring means that UT has trained subject matter experts armed and ready to take action when adverse conditions arise, whether it be an asset or a system issue.
This presentation will explore wireless technology, essential asset monitoring solutions, and remote asset and system health monitoring and show examples of how they improved plant reliability. Examples from the University of Texas-Austin’s Separations Research Program will be used to illustrate how a more reliable plant can be achieved. A demo of UT’s remote system monitoring will be included to show how their remote monitoring system can be used to automatically route alerts to the right personnel at the onset of the condition, whether it be a cavitating pump or an overloaded PC.
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