DETECT FAILURE POINT ON A CURVE VIA SLIDING LINEAR REGRESSION¶
Slides a linear-regression window across the curve and reports the index where R^2 first drops below the regression limit (= the curve stops being linear, signalling failure). Returns failure index/time/value and the pre-failure linear slope/intercept fit on the last good window.
When to use¶
Tagged: curve, failure_detection, linear_regression, sliding_window, r_squared.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Curve | curve_in | vector | — | ✓ | Curve to scan for the failure point; for FD-style calibration this is typically the displacement-vs-time signal. |
| Percentage of Points (sliding window) | percentage_of_points | number | 0.01 | Fraction of total curve points used as the sliding regression window (default 0.01 = 1%). | |
| Regression R^2 Limit | regression_limit | number | 0.9 | R^2 threshold below which the regression window is considered non-linear and failure is declared (default 0.7). | |
| Extrapolate Past Failure | extrapolate_post_failure | select | no | When yes, the worker also emits an Extrapolated Curve output that replaces every post-failure point’s y with a linear extrapolation from the failure anchor along the pre-failure regression slope. Pre-failure points pass through unchanged. Default no — extrapolated_curve mirrors the input curve. | |
| Pre-Failure Smoothing Window | pre_failure_smoothing_window | number | 1 | Number of trailing pre-failure regression windows to average for the reported pre_failure_slope and pre_failure_intercept (and for the post-failure extrapolation when Extrapolate Past Failure = yes). Default 1 — use only the most recent regression (legacy behavior). 2+ averages the last N windows so a single noisy regression near the failure event doesn’t skew the result. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Failure Index | failure_index | scalar | Sample index in the input curve where the sliding regression first failed the R^2 limit. |
| Failure Time | failure_time | scalar | X value (typically time) at the failure index. |
| Failure Value | failure_value | scalar | Y value at the failure index. |
| Pre-Failure Slope | pre_failure_slope | scalar | Slope of the last regression window that satisfied the R^2 limit. |
| Pre-Failure Intercept | pre_failure_intercept | scalar | Intercept of the last regression window that satisfied the R^2 limit. |
| Status | status | string | Human-readable summary including detected index, time, and parameters used. |
| Extrapolated Curve | extrapolated_curve | vector | Curve where every post-failure point’s y is replaced with the pre-failure regression line. Same x-grid as the input. Equals the raw input when Extrapolate Past Failure is no or no failure was detected. |
| Input Curve (Passthrough) | input_curve | vector | Echoes the curve_in input back as an output. Lets downstream visualizers (e.g. curves_overlay) wire BOTH input + extrapolated curves from this single worker without a second edge from the start node. |
Disciplines¶
- data.curve.transform
- engineering.material.characterization
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