Correlation Heatmap

Use Cases

The Correlation Heatmap is designed for exploring the relationships among many parameters at the same time. It can help you discover unexpected correlations that can point to root causes and identify how changes to input fields may affect output fields. The Correlation Heatmap is most helpful early in the exploratory process. After you have identified pairs of interest, you can investigate these pairs in more detail with some of the other tools, such as the Curve Fit Analysis.


The Correlation Heatmap is a multivariate analysis that you can perform on continuous fields on the Cycle and Part models. You can select multiple machines of a given type when performing the analysis on the Cycle model. Using the Cycle model, the Correlation Heatmap can investigate parameters from the same machine type with high granularity. Using the Part model lets you compare fields across different machines, but with lower granularity (the Part model is limited by the serial number or other identifier used to construct the data model).


The Correlation Heatmap is an exploratory tool that allows you to investigate the pairwise relationships among up to 10 parameters by computing the Pearson-R correlation coefficient for each pair. This tool also presents a table of summary statistics for each selected parameter that includes Count, Standard Deviation, Minimum, and Maximum.

How to Use a Correlation Heatmap

The Correlation Heatmap tool is an exploratory data tool that allows you to investigate the relationships between any number of parameters.

NOTE: This tool only works on numeric fields, not categorical fields such as Pass/Fail.

To use a Correlation Heatmap:

  1. On the Analysis tab, under Correlation, click Correlation Heatmap.
  2. On the main Correlation Heatmap screen, select your options on the left. For more details about each option, see Correlation Heatmap Options.
  3. Click Update.

Correlation Heatmap Options

The Correlation Heatmap options include:

  1. Model: You can evaluate two types of parameter relationships:
    • Cycles: Determine if there is a correlation between parameters in the same asset or asset type.
    • Parts: For discrete manufacturers, determine if there is a correlation between parameters across different machines producing the same part type.
  2. Assets: You can select a given asset, or multiple assets of the same type, to monitor. For example, you can select the Cycles model to look at the relationships between the parameters of specific machines. If you are using the Parts model, you need to select a part type.
  3. Relative Range: You can define a date range of data points to analyze for the assets you have selected. For example, select the last 7 days. This option establishes the boundaries for the near real-time data that will be part of your analysis. Select from relative or absolute timeframes.
  4. Data Fields: You can select the specific parameters of interest. There is no limit to the number of parameters that you can select.
  5. Carry-forwards: You can undo the forward-fill effect on any fields. Select from Keep All, First, or Last. For more information, see the table in Options Common to All Analysis Tools.
  6. Update: Click this button to generate your chart.

Tool Output

The Correlation Heatmap tool displays a heatmap of the correlation coefficient between all possible pairs of parameters that are associated with the selected machine type. It provides you with a visualization of the nature of correlation using color (positive in blue or negative in red), and the magnitude of the relationship using intensity or hue.

The value of correlation is represented by the Pearson-R correlation coefficient displayed in each cell.

This is an excellent way for you to discover which parameters are correlated to each other.