Title: Seeing Impacting Clearly: Circular (Polar) Waveform Plots + Autocorrelation (Emerson Machinery Manager)

In vibration analysis, we often talk about “impacting” — but the real value comes when we can prove whether an impact is repeatable (mechanically driven) or random (process/noise).

Two time-domain tools in Emerson AMS Machinery Manager make this very visual:

1) Circular (polar) time waveform plot (0–360°)
A circular plot is the time waveform “wrapped” into one machine cycle (0–360°).
If an impact repeats at the same shaft angle, it shows up as a consistent spike/lobe at the same position on the circle.

✅ In my example (image attached), the “BEFORE” plot shows a spiky / star-like ring — classic impulsive behaviour.
✅ The “AFTER” plot becomes more uniform and circular, indicating reduced impacting and more stable running behaviour.

2) Why autocorrelation matters here
Autocorrelation is a repeatability detector for the waveform:
Strong autocorrelation peaks at the running period → the event is synchronous/cycle-locked (often mechanical).
Weak/rapidly decaying autocorrelation → more random/non-synchronous impacts (or process-driven behaviour).

So, autocorrelation answers the question:
“Is this impact repeating each revolution (or each cycle), or is it random?”
Practical tips for clean “before vs after” comparisons
To make the story defensible:
Use the same point/direction, same sensor and mounting method
Keep waveform settings the same (record length, bandwidth/Fmax, filtering, averages)
Avoid auto-scale tricks when presenting (lock scales if possible)
Capture enough cycles (and use tach/key phasor if available)

Bottom line: Circular plots show you where impacts occur in the cycle, and autocorrelation tells you how repeatable they are. Together, they’re a strong way to demonstrate improvement after corrective action — beyond just overall vibration numbers.