Why is HanPHI needed?
Most breakdowns, approximately 89%, are unrelated to the lifespan of the equipment. For effective plant operation, early warning of issues not related to the age of the equipment is necessary. HanPHI enables a plant to perform predictive maintenance through the identification of breakdown symptoms in their early stage.
How many tags can HanPHI process with a single server?
HanPHI can process 5,000 tags with a single sever.
How long is the scan interval?
The data scan intervals are: event data minimum 1 ms, analog data minimum 10 ms.
How long is the predicted value calculation interval?
The predictive value calculation interval is minimum 100 ms.
How accurately can HanPHI predict the data?
HanPHI models are built using robust mathematical algorithms and several recursive calculations are performed to verify the calculated results. The accuracy of the predicted result approaches 100% accuracy.
Can HanPHI process data without loss?
Yes. Every data point is scanned at buffered at very high speed. Each and every piece of data is timestamped with the quality. There is no loss of data even though several hundred thousand events occur at the same time.
What type of database program does HanPHI use?
HanPHI uses a relational database for storing static information only.
Should I rebuild the prediction model after equipment maintenance or O/H?
Yes. After equipment maintenance and O/H, there may be changes in equipment parameters or the equipment may be replaced.
How is the health index calculated in HanPHI?
HanPHI can read and analyze the historical normal conditions of all plant equipment to predict the expected normal condition of all plant equipment. HanPHI measures the difference between the real-time and expected normal operating condition to calculate the health index in terms of a percentage value. A higher health index represents a better health status of plant equipment.
What is the HanPHI Trend? How can a user utilize the HanPHI Trend?
The Trend is a graphical representation of changing plant conditions and tag values. The Y-axis of trend shows the tag value of the plant and the X-axis shows the time. The trend is very useful to find out the development of a fault in the plant equipment. Using the trend display, you can view the behavior of tags from the same SuccessTree group, model group, or a group you created.
What kinds of anomalies can HanPHI detect in advance?
HanPHI can detect potential equipment failure, process failure, sensor failure, and improper training models in advance.
Is there a minimum learning period of past data for forecasting model construction?
It is ideal to construct the learning model by reflecting the normal plant operation for one year of data that takes seasonal change and equipment lifespan into account. However, HanPHI is able to generate a model adjusted to the purpose of a user by setting the learning period and range for as little as one day.