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OperatingRangeEvents¤

Analyze the operating envelope of a signal — what ranges it operates in and when the operating point shifts.

Module: ts_shape.events.engineering.operating_range
Guide: Process Engineering

When to Use¤

Use for operating envelope analysis. Tracks what ranges your process actually operates in and detects regime changes that may indicate product changeovers or process shifts. Useful for building golden-run profiles and detecting off-spec operation.

Quick Example¤

from ts_shape.events.engineering.operating_range import OperatingRangeEvents

analyzer = OperatingRangeEvents(
    df=process_data,
    uuid="extruder_speed_01"
)

# Get the operating envelope per hour
envelope = analyzer.operating_envelope(window="1h")

# Detect when the operating point shifts significantly
shifts = analyzer.detect_regime_change(
    window="1h",
    shift_threshold=2.0
)

# Calculate time in a target operating range
in_range = analyzer.time_in_range(
    lower=180.0,
    upper=220.0,
    window="1h"
)

# Value distribution histogram
distribution = analyzer.value_distribution(n_bins=10)

Key Methods¤

Method Purpose Returns
operating_envelope(window='1h') Per-window min/max/mean envelope DataFrame with envelope stats
detect_regime_change(window='1h', shift_threshold=2.0) Operating point shifts DataFrame of regime changes
time_in_range(lower, upper, window='1h') Time in target range per window DataFrame with in-range durations
value_distribution(n_bins=10) Value histogram DataFrame with bin counts

Tips & Notes¤

Use time_in_range for OEE-style metrics

time_in_range() gives you the fraction of each window spent within spec. Aggregate this over a shift or day to get a process-level "in-spec percentage" that complements OEE availability and quality metrics.

Related modules

See Also¤