HanAra Success Stories
Improved Facility Monitoring through Fault Detection
Before HanPHI, a large chemical organization was looking for predictive maintenance software to improve productivity and production by leveraging equipment sensor data and machine learning. Here is the success story of introducing HanPHI, an AI-based predictive maintenance solution, into their chemical operations.
To reduce unexpected equipment failures, unplanned maintenance activities, and outage time, UT Austin chose HanAra Software’s HanPHI™ solution as its condition-based predictive monitoring solution.
Since installing HanPHI in 2012, KOEN has increased its annual electricity output and the lifecycle of its assets and decreased its maintenance costs, repair time, fuel usage, and unscheduled downtime.
In 2014, HanAra professionals replaced the existing historian software at UT-Austin with HanPrism. The HanPrism data migration tool migrated every single piece of data from 2009 to 2015, representing approximately 10,000 points.
KOGAS strives to minimize risk throughout the process from gas production to supply by constantly running safety diagnostics and emergency drills, but its existing plant information system data infrastructure did not help KOGAS improve its safe and efficient operation.
As systems and equipment in process plants become more complex and sophisticated, gaining actionable intelligence from the vast amount of generated data has become one of the key success factors. Extracting accurate and meaningful information from the data helps KOEN achieve operational excellence.
The high variability of the electricity generating capability hourly, daily, and seasonally exposes wind farms to frequent equipment failures and operational inefficiencies. In order to alleviate concerns of wind station capabilities, Yeong-Am needed a solution to improve operations and reduce inefficiencies.