Pandas + Historians: Fast Root-Cause Analysis

Traditional historians are great at storing process data — but not always at analyzing it. By combining Python’s Pandas library with historian APIs, engineers can perform deep root-cause analysis in minutes, not hours.

Accessing Historian Data

• Most modern historians (AVEVA, PI, Ignition, Zenon) expose REST or ODBC interfaces.
• Use lightweight clients like requests or pyodbc to query time-series data directly into Pandas.
• Resample with df.resample('1S').mean() for clean signal alignment.

Analytical Patterns

• Use corr() to find correlated tags before failures.
• Plot control valve behavior with matplotlib or plotly for transient events.
• Detect drifts with rolling standard deviation and ewm() smoothing.

Example Use Case

A packaging plant combined 6 months of historian data with Pandas for temperature and torque trends. The analysis pinpointed a miscalibrated drive — cutting unplanned downtime by 22%.

Related Articles

• Python Next to PLCs: Safety, Sandboxing, and IPC
• Testing Python Pipelines in a Simulated Plant
• Scheduling Data Jobs in OT: Cron, MQTT, and Triggers

Conclusion

Pandas turns raw historian data into insight. For engineers fluent in scripting, it’s the fastest way to validate root causes before the next shift starts.