Reactor Sensor Correlation & Anomaly Stream

● LIVE — NORMAL

Stream Controls

Sensor noise 0.35

Correlation window (samples) 80

Anomaly threshold (|z|) 2.5

Live Readouts

Correlation R

0.00

ΔT/Δt

0.00 °C/s

Temperature

22.0 °C

Pressure

101.3 kPa

Residual z-score

0.00

Anomalies flagged

How it works

A reactor's temperature is held near a setpoint by a proportional thermostat, a recursive feedback loop sampled once per tick:

u(t)  = Kp · (Tset − T(t))                  (controller output)
T(t+1) = T(t) + [ −kloss·(T(t) − Tamb) + u(t) ] · Δt + noise
                                              (recursive plant equation y(t+1)=f(y(t),u(t)))

Pressure is physically coupled to temperature (a sealed vessel builds pressure as it heats), so under normal operation:

P(t) = Pbase + α·(T(t) − Tamb) + noise

Each new sample is pushed into a sliding window of the last N points (N = correlation window). From that window the simulator computes the rate of change, the Pearson correlation coefficient between the two streams, and an anomaly score from the regression residual:

ΔT/Δt = (T(t) − T(t−1)) / Δt
R = Cov(T,P) / (σ_T · σ_P)                    (Pearson correlation, window)
ê = P − (slope·T + intercept)                 (least-squares residual)
z = (ê − mean(ê)) / std(ê)                    (anomaly score)

Trigger Malfunction forces a brief decoupling — temperature drops while pressure spikes independently, exactly the "pressure up, temperature down" fault pattern real plant operators watch for. R collapses toward zero (or negative) and |z| crosses the alert threshold, flagging the correlated cloud's outliers in red.

Normal sample (T, P, recency) Flagged anomaly Regression trend line

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