If you’re managing AF Analyses in the PI System, you’ve probably spent time in PI System Explorer’s Performance tab under Analysis Management. It’s a great starting point for diagnosing calculation performance but it only tells part of the story.
In this post, we’ll break down what each of these metrics means, what they reveal (or don’t), and how Tycho Data Osprey can enhance your troubleshooting process by adding lineage insights and runtime visibility.
📊 What the PI System Explorer Performance Tab Tells You
When you open PI System Explorer → Management Plug-In → Analyses → Service Summary tab, you’ll find several helpful metrics:
| Tile | What It Tells You |
|---|---|
| Service Startup Time | Time it took for the AF Analysis Service to start up and begin calculating. |
| Calculations per Second | How many calculations are running on average each second; higher means more activity. |
| Skipped Calculations | How many calculations were missed since the service started usually due to lag, config errors, or bottlenecks. |
| Max. Calculation Lag | Worst-case latency between when an analysis should run and when it actually did. |
| Cache Hit to Miss Ratio | How often analysis inputs were found in cache vs. having to be fetched; higher is better. |
| Max. Update Duration | Longest time taken to update any AF analysis input in cache. |
| Highest Trigger Ratio | Template with the worst ratio of elapsed time to trigger time; ideally less than 1. |
It’s great, but it leaves a lot of unanswered questions.
🚨 The Real Troubleshooting Challenge
When you see:
- Skipped Calculations climbing
- Calculation Lag spiking
You naturally want to ask:
- Which analyses are causing the issue?
- What other assets or calculations depend on them?
- Is this problem isolated or part of a broader performance issue?
That’s where PI System Explorer alone falls short.
🔍 How Osprey Adds Depth: Lineage + Runtime Insights
Osprey enhances your troubleshooting workflow in two key ways:
✅ 1. AF Analysis Lineage Mapping
- See which AF Analyses feed other AF Analyses
- Trace which PI Tags and AF Attributes are inputs to each calculation
- Identify downstream impacts if a calculation fails or lags
- Avoid breaking dependencies when decommissioning or adjusting calculations
Example:
“This slow-running analysis feeds 12 other analyses and 3 PI Vision displays; better prioritize this fix.”
✅ 2. Analysis Runtime Metrics by Asset & Template
While the Performance tab aggregates metrics for the whole service, Osprey can show you:
- Analysis runtimes and lag times per template
- Identify bottlenecks by asset, template, or server
- Flag calculations with frequent timeouts or skipped evaluations
- Highlight long-chain dependencies that create cumulative performance drag
Example:
“All analyses based on the
Compressor_Runtime_Statustemplate have a trigger ratio above 1.3 — let’s optimize that.”
⚙️ Bringing It Together
By combining:
- Performance metrics from the PI System
- Lineage and dependency tracing from Osprey
- Per-analysis runtime visibility
…you get a much clearer picture of what’s slowing your system down, what’s affected, and where to focus your fixes.
💡 Conclusion: Smarter AF Analysis Troubleshooting
The built-in tools in PI System Explorer give you a helpful snapshot — but when you need to investigate complex dependencies, avoid operational disruptions, and prioritize fixes, Osprey helps you go deeper.
Stop guessing. Start troubleshooting with context.
Ready to See It in Action?
If your team depends on PI System data to keep your plant running safely and efficiently — Osprey is built for you.
Tycho Data 