Shifting the focus of data collection and analytics to the edge of the network improves equipment reliability while preventing costly, and potentially disastrous, downtime.
By Seyed Mirsepassi, VP of Solutions, FogHorn Systems
The unexpected failure of a steam turbine can create substantial disruption, damage, and economic loss, not only to its power plant but also the downstream power grid and the business and customers who depend on a reliable and continuous source of electricity. To prevent this, predictive maintenance has become a key part of the modern maintenance department. More power utilities are deploying such processes to maximize the reliability of their equipment by detecting potential failures before significant problems arise.
It begins with software systems that can collect and establish historical performance baseline data from each turbine as well as the entire power plant. Edge intelligence technology can then compare this baseline data to the real-time performance monitored on a continuous basis and trigger an alarm if the real-time performance data is outside of the baseline data’s acceptable range.
Edge intelligence refers to a software solution based on fog computing which extends data processing and analytics closer to the “edge” where Industrial Internet of Things (IIoT) devices and sensors reside. Maintaining close proximity to these edge devices rather than sending all data to a distant centralized cloud for processing, minimizes latency allowing for maximum performance, faster response times, and more effective maintenance and operational strategies. This is becoming increasing important in cases where even a few seconds might mean the difference between a safe and orderly response and major equipment failure.
With predictive maintenance, any variance from the “expected behavior” can trigger an alert. These triggers could be based on predetermined performance rules, such as temperature or vibration outside of an acceptable range or be based on deployment of predictive models at the edge that account for historical data. The use of predictive analytics enables users to explore potential root causes and appropriate remedies.
Power plant maintenance is a major undertaking so deployment of predictive analytics at the edge allows power plant operators to proactively schedule maintenance in a non-disruptive fashion to avoid outages altogether and to do so in the most cost effective manner. One of the advantages of an intelligent edge solution is that it can respond immediately to any anomaly or malfunction before it becomes a much more serious and costly issue. It also eliminates the need to upload massive amounts of data to remote data centers because many maintenance issues can now be resolved on site using analytics and processing applications deployed at the edge.
The data center or cloud will continue to play a key role by providing overall system management as well as analytics and machine learning functions that require the computing power of a cloud/datacenter. However by relying more on edge intelligence for critical latency-sensitive functions, utility companies can reduce overall bandwidth and data processing costs and operational delays by sending only non-critical information to the data center or cloud for analysis.
In summary, a predictive maintenance solution that utilizes edge intelligence can maximize revenue and customer satisfaction by reducing maintenance and repair costs and by preventing disruptive downtime and energy blackouts.