Operating Condition Detection - Prediction
This topic describes the Operating Condition Detection - Prediction component.
Features
The Operating Condition Detection - Prediction component detects operating conditions in real time. It uses models generated by the Operating Condition Detection - Training component.
This component supports only models generated by the Operating Condition Detection - Training component.
How the calculation logic works
This component analyzes industrial data using unsupervised learning methods, such as clustering and dimensionality reduction. It builds separate models for data from different operating conditions.
Parameters
IN port - Input parameters
Parameter | Description | Required | Input data type |
Model application | Select a model type and a specific model. Then, configure the input data for the model. | Yes | Feature variables: Integer or floating-point number Note An exception is thrown if non-numeric data is present. |
OUT port - Output parameters
Parameter Name | Description | Required | Output data type |
OUT | The output parameter configuration corresponds to the target variable name shown in the output preview of the model application. | No | Integer or floating-point number |
Output quality codes
Output quality codes are processed as follows:
If the quality code of all input variables is >= 192, the quality code of the output variable is 192.
If the quality code of any input variable is in the range [0, 192), the quality code of the output variable is 0.
If the quality code of all input variables is -1, or if some are -1 and the rest are >= 192, the quality code of the output variable is -1.