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Controlled straight-line extrapolation · non-normative worksheet

Documented Linear Threshold-Projection Worksheet

Fit one documented indicator history with ordinary linear least squares and calculate its analytical crossing of a user-sourced threshold inside a declared extrapolation horizon.

no invented confidenceno universal vibration limitanalytical crossingcurrent ISO status
This is not an ISO RUL calculator or a failure-probability model. ISO 13381-1:2025 provides current guidance and requirements for prognosis processes; its public scope does not establish this worksheet’s regression formula, a universal alarm threshold, or a rule that converts R² into confidence. The withdrawn ISO 13381-1:2015 edition is not presented as current.

Controlled projection inputs

Use one indicator, one operating regime and one time coordinate. The last row is treated as the current observation.

No default is supplied. The threshold must use the same quantity and unit as every y value.
Same time-coordinate unit. A crossing beyond this user-controlled horizon is not reported.
PunktTime coordinate tIndicator y
1
2
3
4
5
6
7
8
Enter at least three complete pairs. Times must be finite, non-negative and distinct; order may be arbitrary. Blank rows are ignored. This three-point minimum is a mathematical UI rule, not an ISO adequacy criterion.
Required projection confirmation

Reference projection result

Projected remaining span Δt
Straight-line threshold projection, not certified RUL.
Projected crossing coordinate tL
Fitted slope b
Descriptive R²
In-sample fit statistic only; not confidence.
Residual standard error s
√[Σ(yi−ŷi)²/(n−2)] in indicator units.
Extrapolation/baseline ratio
Directional-step conflicts
Observed adjacent steps opposing the selected direction.
No confidence level, prediction interval, failure probability, ISO conformity, universal threshold or maintenance/safety verdict is produced.

Equations, status and model boundary

For n ≥ 3 complete observations and the straight-line model ŷ(t)=a+bt, ordinary unweighted least squares gives:

b = Σ[(ti−t̄)(yi−ȳ)] / Σ[(ti−t̄)²] · a = ȳ−bt̄
tL = (L−a)/b · Δt = tL−tlast · s = √{Σ[yi−ŷ(ti)]²/(n−2)}

The calculation uses an analytical crossing, not a stepped search. It reports a result only when the fitted slope points toward a threshold beyond the last observation, the crossing lies in the future, and the remaining span does not exceed the user-declared horizon.

R² is descriptive. It summarizes in-sample lack of fit relative to variation around the sample mean. It is not a confidence percentage, probability of correct prediction, prediction interval, model-validation result or evidence that the mechanism will remain linear outside the data.

Exact numerical checks

Data and controlSobivusProjection
(0,1),(1,3),(2,5); upper L=9; horizon=10ŷ=1+2t; R²=1; s=0tL=4; Δt=2
(0,10),(2,8),(4,6); lower L=2; horizon=10ŷ=10−t; R²=1; s=0tL=8; Δt=4

Standard and source classification

ClaimKlassifikatsioonInspected source
Prognosis-process guidance and requirementsCurrent ISO framework; no regression equation inferred from public scopeISO 13381-1:2025, edition 3, published 2025-09
ISO 13381-1:2015 statusWithdrawn 2025-09-02 and revised by the 2025 editionOfficial ISO lifecycle card
Straight-line parameter fitting by minimizing squared residualsGeneral statistical method, not an ISO 13381 formulaNIST/SEMATECH e-Handbook §4.1.4.1 and §4.4.3.1
R² interpretationDescriptive in-sample statistic; no confidence mappingNIST/SEMATECH glossary and process-model limitations

ISO 13381-1:2025 official card — current edition, status and public scope.

ISO 13381-1:2015 official lifecycle card — withdrawn edition and replacement.

NIST/SEMATECH linear least-squares guidance ja R-squared glossary — method and interpretation.

Accessed: 15 July 2026. Only public ISO scope/status information was used. No inaccessible ISO clause, formula, threshold or acceptance rule was invented.

Screening projection only. A defensible prognosis requires a validated degradation mechanism, representative data, measurement uncertainty, operating-regime controls, threshold provenance, model validation, uncertainty treatment and consequences-based engineering review. Outliers, autocorrelation, heteroscedasticity, interventions and regime changes can invalidate ordinary least-squares extrapolation.
Revision: scientific audit 2026-07-15 · English source page · Straight-line reference only · Preserve the complete input and source record with every result.
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