HanPHI: Advanced Pattern Recognition Solutions
Our intelligent predictive-modeling and health-indexing technologies make HanPHI a powerful solution that has significant benefits for system operations. Early warnings identify areas for predictive maintenance, reducing maintenance costs, unscheduled downtime, and equipment failures. With HanPHI, you can eliminate potential operational risks, extend equipment life cycles, and increase asset reliability, efficiency, and safety all within a limited budget.
Real-World Savings From HanPHI
Model Builder and Executer
The HanPHI Builder builds prediction models through a machine learning algorithm and advanced pattern recognition using historical, fault-free normal operation data. Users can retrain models based on time duration, resolution, and user-defined criteria to ensure an accurate prediction model.
The health index is based on the difference between real-time data and the expected model data. The index is for the whole operation including selected major systems, equipment, and signals. The intuitive index from 0 to 100% gives any member in the organization a clear understanding of the current status of equipment or site. Learn More.
The SuccessTree provides hierarchical groups of systems, subsystems, and signals to represent health status indices from the operations level to the individual sensor. The SuccessTree automatically tracks a signal with the lowest index affecting the overall index, enabling fast and effective root cause analysis. Learn More.
HanPHI’s early detection provides early warning for preventing and reducing equipment failure. It provides indication of systems’ abnormalities to avoid equipment failure through planned outages. Learn More.
KOREA SOUTH-EAST POWER
Korea South-East Power Company (KOEN) is a major power generation company, providing approximately 10,000 megawatts of electricity at its 12 plants. KOEN generates electricity for metropolitan areas and southern Korea. KOEN generates approximately 10% of South Korea’s annual net electricity.
Since installing HanPHI in 2012, KOEN has proactively managed its plants. With HanPHI, 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.
THE UNIVERSITY OF TEXAS AUSTIN
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.
With the implementation of HanPHI, HanAra provided the UT Austin team with the information to simplify and speed-up the decision-making and maintenance process. With a centralized location for data and the capacity to utilize predictive maintenance, HanPHI also eliminates redundant and unnecessary maintenance and support costs.
KHNP is the largest Korean electric power company, generating approximately 31.5% of the total generated electric power in Korea. KHNP operates 82 units, including solar, hydro, geothermal, bio, hydrogen, and more, with a total production capacity of 27,857 MW.
With growing increase in operational data available at plants, KHNP turned to HanPHI to ensure the technical safety of their facilities and to manage data at a centralized control and monitoring center.
Ways to use HanPHI
HanPHI provides a plant health index to give you early warnings of potential and hidden equipment failures. Since HanPHI is comparing real-time values to HanPHI expected values, it also alerts you of potential sensor issues, including mismapped sensors.
Ways to use HanPHI
The value of HanPHI is that it represents the current operating status of your equipment, systems, plant, and fleet. By using the historical normal, fault-free data to create models, you have a better understanding of how your equipment operates in your environment and in your process.