Visualizing multivariable regression - Manual - Performance Insight - Industrial Edge - Industrial Edge App - Performance Insight is a cloud-based application that provides a comprehensive overview of the performance of your industrial assets. It enables you to monitor and analyze the performance of your assets in real-time, identify potential issues, and take corrective actions to optimize asset performance. - Industrial Operations X - Industrial Edge - Issue identification - Performance Insight - Performance analysis - Corrective actions - Cloud-based application - Optimization - Real-time monitoring - Asset performance

Performance Insight

Portfolio
Industrial Edge
Product
Performance Insight
Software version
v1.21
Edition
06/2025
Language
English

161061860107-d2e5626

①: Selection of the output parameter

②: Marking the dashboard as Favorite

③: Displaying more dashboards on the asset

④: Selection of a model that describes the output parameter

⑤: Selection of the time range for the analysis

⑥: Selection of the calculation interval

⑦: Menu: "Multivariable regression list", "Edit selected multivariable regression", "Export data"

⑧: Characteristics of the model: Formula, F-test, correlation coefficient

⑨: Graphic display of the deviation

⑩: Showing and hiding the individual parameters and the limit values

⑪: Graphic representation of the output parameter and the regression

To visualize the multivariable regression in the dashboard, follow these steps:

  1. Select the output parameter.
  2. Select a model that describes this parameter.
  3. Select the period for which the multivariable regression is visualized.
    You can apply the model to any time range in the past.
    See also: User-defined periods.
  4. Select the calculation interval.
    With the calculation interval you determine the granularity of the visualization. You determine, for example, whether data values of each minute or of each hour are considered.
  5. Select which trends you want to show or hide.
    The following trends are offered:

    • value trend of the output parameter
    • regression
    • deviation of the actual values from the model (Deviation)