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Index

quality ¤

Quality Events

Detectors for quality-related events: outliers, statistical process control, and tolerance deviations over time series.

  • OutlierDetectionEvents: Detect and group outlier events in a time series.
  • detect_outliers_zscore: Detect outliers using Z-score thresholding and group nearby points.
  • detect_outliers_iqr: Detect outliers using IQR bounds and group nearby points.
  • detect_outliers_isolation_forest: Detects outliers using sklearn's IsolationForest algorithm.

  • detect_outliers_mad: Detects outliers using the Median Absolute Deviation (MAD) method.

  • StatisticalProcessControlEvents: Apply Western Electric rules to actual values using tolerance context to flag control-limit violations.

  • control_limits: Compute mean and ±1/±2/±3 standard-deviation bands from tolerance rows.
  • detect_violations: Apply selected rules and emit event rows for violations.
  • rule_1: One point beyond the 3-sigma control limits.
  • rule_2: Nine consecutive points on one side of the mean.
  • rule_3: Six consecutive points steadily increasing or decreasing.
  • rule_4: Fourteen consecutive points alternating up and down.
  • rule_5: Two of three consecutive points near the control limit (between 2 and 3 sigma).
  • rule_6: Four of five consecutive points near the control limit (between 1 and 2 sigma).
  • rule_7: Fifteen consecutive points within 1 sigma of the mean.
  • rule_8: Eight consecutive points on both sides of the mean within 1 sigma.
  • detect_rule_violations: Apply the selected Western Electric rules in one vectorized pass.
  • dynamic_control_limits: Calculate dynamic control limits that adapt over time.
  • detect_cusum_shifts: Detect process shifts using CUSUM (Cumulative Sum) control chart.

  • interpret_violations: Add human-readable interpretations to rule violations.

  • ToleranceDeviationEvents: Flag intervals where actual values cross/compare against tolerance settings and group them into start/end events.

  • detect_deviations: Build grouped deviation events with event UUIDs.

  • capability_indices: Calculate process capability indices (Cp, Cpk, Pp, Ppk).

  • AnomalyClassificationEvents: Classify anomaly types in numeric signals.

  • classify_anomalies: Detect and classify by type (spike/drift/oscillation/flatline/level_shift).
  • detect_flatline: Signal stuck at constant value.
  • detect_oscillation: Periodic instability detection.

  • detect_drift: Short-term slope-based drift events.

  • SignalQualityEvents: Signal data quality monitoring.

  • detect_missing_data: Find gaps exceeding expected sampling frequency.
  • sampling_regularity: Inter-sample interval statistics per window.
  • detect_out_of_range: Flag values outside physical/expected bounds.

  • data_completeness: Percentage of expected samples received per window.

  • CapabilityTrendingEvents: Track process capability over rolling time windows.

  • capability_over_time: Cp/Cpk/Pp/Ppk per time window.
  • detect_capability_drop: Alert when Cpk falls below threshold.
  • capability_forecast: Extrapolate Cpk trend to predict threshold breach.

  • yield_estimate: Estimated yield, DPMO, and sigma level per window.

  • SensorDriftEvents: Detect calibration drift in inline sensors.

  • detect_zero_drift: Track mean offset from baseline per window.
  • detect_span_drift: Track measurement sensitivity changes over time.
  • drift_trend: Rolling linear trend analysis on signal statistics.

  • calibration_health: Composite health score per window.

  • MultiSensorValidationEvents: Cross-validate redundant inline sensors.

  • detect_disagreement: Flag windows where sensor spread exceeds threshold.
  • pairwise_bias: Mean difference between each sensor pair per window.
  • consensus_score: Per-window measurement consensus across sensors.
  • detect_outlier_sensor: Find the sensor furthest from the group.

  • sensor_uuids: Deprecated read-only alias of signal_uuids.

  • GaugeRepeatabilityEvents: Measurement System Analysis (Gauge R&R).

  • repeatability: Equipment Variation (EV) per part.
  • reproducibility: Appraiser Variation (AV) across operators.
  • gauge_rr_summary: Full Gauge R&R table with %GRR and ndc.

  • measurement_bias: Compare measurements to known reference values.

  • ChangePointEvents: Offline change-point detection (PELT, binary segmentation).

  • detect_change_points: Level (and spread) shifts with before/after stats.

  • segment_statistics: Stable segments between change points.

  • MultivariateSPCEvents: Hotelling T² multivariate SPC.

  • hotelling_t2: T² with Phase I / Phase II control limits.
  • contributions: Per-variable T² decomposition.

  • detect_out_of_control: Out-of-control runs with the top contributor.

  • MeasurementUncertaintyEvents: GUM-based measurement uncertainty estimation.

  • type_a_uncertainty: Statistical (Type A) uncertainty per window.
  • type_b_uncertainty: Uncertainty from external sources (calibration, resolution).
  • combined_uncertainty: Combined and expanded uncertainty (u_c, U).

  • uncertainty_budget: Contribution breakdown by source.

  • EnvironmentalCompensationEvents: Environmental effect compensation.

  • detect_correlation: Pearson correlation with environmental signal per window.
  • sensitivity: Environmental sensitivity coefficient estimation.
  • compensated_signal: Apply linear compensation model.

  • detect_env_exceedance: Flag out-of-range environmental conditions.

  • CalibrationValidationEvents: Multi-point calibration validation.

  • linearity_assessment: Residual analysis at calibration points.
  • calibration_curve: Polynomial fit to calibration data.
  • detect_recalibration_need: Monitor bias/drift for recal triggers.

  • calibration_stability: Bias stability scoring over time.

  • SamplingResolutionEvents: Sampling and resolution analysis.

  • detect_quantization: Detect A/D quantization in measurements.
  • effective_resolution: Estimate effective vs theoretical resolution.
  • detect_aliasing: Check for Nyquist violations via FFT.

  • sampling_jitter: Measure timing regularity of sampling.

  • DataGapAnalysisEvents: Analyze gaps and coverage in signal data.

  • detect_gaps: Locate all gaps longer than a threshold.
  • gap_summary: Aggregate statistics across all gaps.
  • coverage_by_period: Data coverage percentage per time window.

  • interpolation_candidates: Gaps small enough to interpolate safely.

  • ValueDistributionEvents: Examine signal distribution over time.

  • detect_mode_changes: Detect shifts between distinct operating modes.
  • detect_bimodal: Test whether the signal has a bimodal distribution.
  • normality_windows: Flag time windows with non-normal distributions.
  • percentile_tracking: Track selected percentiles over time windows.