Troubleshooting Common PI AF Analysis Runtime Errors: How Osprey Can Help

When working with PI AF (Asset Framework) Analyses, users often encounter runtime errors that can disrupt critical calculations and affect system performance. These errors can range from simple configuration mistakes to more complex data issues. If you’ve ever faced an error like “No Output Defined,” “Calc Failed,” or “No Data,” you’re not alone and more importantly, these issues can be resolved.

In this post, we’ll explore some of the most common PI AF Analysis runtime errors, their causes, and troubleshooting steps you can take. We’ll also show how Osprey, our data observability tool, can assist you in diagnosing these errors more effectively, saving you time and improving data reliability.

Common PI AF Analysis Runtime Errors and Troubleshooting Steps

1. “No Output Defined”

  • Cause: This error occurs when an analysis lacks a defined output attribute, even though it may appear to be set correctly.
  • Troubleshooting:
    • Double-check that the output attribute is correctly mapped in the analysis configuration.
    • Ensure the attribute’s data reference is set to “Analysis” and not “None.”
    • Look for syntax errors that could be preventing the output from being set.
    How Osprey Can Help: Osprey’s analysis performance tracking shows the detailed configuration of each analysis, helping you identify missing or incorrect output attributes that lead to errors.

2. “PI Point Not Found” or “Pt Created”

  • Cause: This error happens when the PI Point associated with the analysis output exists but hasn’t been initialized with data.
  • Troubleshooting:
    • Ensure the PI Point is properly configured and associated with the analysis output.
    • Check if the PI Point is in a “Pt Created” state, indicating it hasn’t received data yet.
    • Allow sufficient time for the PI Point to populate data if it’s newly created.
    How Osprey Can Help: Osprey tracks the runtime metrics of AF analyses, so you can monitor whether the PI Points associated with an analysis are receiving data in real-time and resolve issues before they impact operations.

3. “Calc Failed”

  • Cause: A general calculation failure during execution, often due to incorrect syntax or invalid data.
  • Troubleshooting:
    • Review the analysis expression for any errors or unsupported functions.
    • Verify that all input attributes are receiving valid data and have the correct data types.
    • Make sure the PI Points referenced in the calculation are accessible and properly configured.
    How Osprey Can Help: Osprey can help you monitor runtime performance and identify which specific calculations are causing failures, giving you more granular insight into what needs to be fixed.

4. “No Data” or “Bad Data”

  • Cause: This occurs when the analysis receives input data that is either missing or flagged as bad.
  • Troubleshooting:
    • Confirm that the input attributes are configured correctly and receiving data.
    • Check for data quality issues, such as missing timestamps or invalid values.
    • Use functions like BadVal() or NoOutput() to handle bad or missing data in the analysis expression.
    How Osprey Can Help: Osprey helps you track data quality issues across your PI tags and AF attributes, giving you an overview of missing or bad data that could be affecting your analysis.

5. “Analysis in Warning or Error State”

  • Cause: An analysis enters a warning or error state, preventing it from executing correctly.
  • Troubleshooting:
    • Disable the analysis temporarily while you resolve the issue.
    • Review detailed error logs to identify the root cause.
    • Ensure that all input attributes are configured correctly and receiving valid data.
    How Osprey Can Help: Osprey tracks the status of each analysis, alerting you when an analysis enters a warning or error state. This allows you to quickly identify and address issues to minimize downtime.

Osprey’s Role in Troubleshooting PI AF Analysis Errors

While many of these issues can be addressed by manually reviewing analysis configurations, Osprey offers a powerful solution for diagnosing PI AF analysis errors more efficiently. Osprey tracks key runtime metrics for your PI AF Analyses, including execution status, error states, and performance bottlenecks. It provides insights into the health of your analyses, making it easier to pinpoint the source of errors like those described above.

By leveraging Osprey’s monitoring capabilities, you can:

  • Gain visibility into real-time performance of your AF Analyses.
  • Quickly identify and resolve data quality issues that may be impacting calculations.
  • Stay on top of changes in your PI System environment, including any modifications to tags, AF attributes, or displays that may affect analysis performance.

With Osprey’s proactive monitoring, you no longer have to rely on guesswork or manual troubleshooting to address PI AF Analysis errors. You can quickly identify bottlenecks, optimize your calculations, and ensure that your data is always accurate and reliable.

Conclusion

PI AF Analyses are a powerful tool for performing complex calculations and automating data workflows. However, runtime errors are an inevitable part of working with them. By understanding common PI AF Analysis errors and using tools like Osprey to track performance and identify issues, you can minimize downtime, improve the accuracy of your data, and keep your operations running smoothly.

Ready to See It in Action?

If you’re facing any of the runtime errors mentioned above, Osprey can help you troubleshoot faster and more effectively. Reach out to us today to learn more about how Osprey can streamline your PI AF Analysis workflows and enhance your data quality efforts.

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