A powerful predictive analytics software that identifies impending equipment failure days, weeks, or months in advance.
This intelligent software learns, models, and analyzes data to provide actionable early warnings to plant operators, engineer, and managers before a catastrophic failure occurs. With HanPHI, plant personnel implement predictive maintenance, keeping their valuable assets in optimal condition.
Prioritize maintenance jobs, depending on intensity of equipment abnormality represented by the health index. Optimize resources based on the condition of the plant(s). Minimize unnecessary maintenance activities.
Improved Plant Availability
Reduce shutdown time by identifying equipment failures in advance. Maintain the constant quality of production.
Our intelligent predictive-modeling and health-indexing technologies make HanPHI a powerful solution that has significant benefits for plant 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 within your limited budget.
Model Builder and Executer
The HanPHI Builder builds prediction models based on historical normal operation data. Users can automatically 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 predictive model data. The index is for the whole plant with the selected major systems, equipment, and signals.
The SuccessTree provides hierarchical groups of plant systems, subsystems, and signals to represent health status indices from the plant level to the individual sensor. The SuccessTree automatically tracks a signal with the lowest index affecting a whole plant index.
Early detection provides early warning for preventing and reducing equipment failure. It provides indication of systems’ abnormalities to avoid equipment failure through planned outages.
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 cost.
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 cost, repair time, fuel usage, and unscheduled downtime.
Since installing HanPHI, the Yeong-Am Wind Farm has improved operational efficiency and reduced equipment failure significantly. Yeong-Am Wind Farm is now the fourth largest wind farm in South Korea. Every day, the farm provides electricity (90,000 watts/hour) to a population of 20,000 in the southwestern part of South Korea.