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Remaining Useful Life (RUL) Prognostics Calculator

Enter trend data points, choose a regression model, set a failure threshold, and estimate the remaining useful life with confidence level per ISO 13381.

ISO 13381-1 Linear / Exponential / Polynomial Up to 8 Points
#Time (t)Parameter Value (y)
Quick presets

Results

Estimated Remaining Useful Life
Predicted Failure Time
Model R² (Goodness of Fit)
Confidence Level
Current Rate of Change

Prognostics per ISO 13381-1

ISO 13381-1 defines a framework for condition monitoring and prognostics of machines. The core idea is to track a health indicator over time, fit a degradation model, and extrapolate to a predefined failure threshold.

Regression Models

Three models are supported for fitting trend data:

  • Linear: y(t) = a + b·t — suitable for steady, constant-rate degradation
  • Exponential: y(t) = a·eb·t — suitable for accelerating degradation (e.g. bearing wear)
  • Polynomial (2nd order): y(t) = a + b·t + c·t² — suitable for non-linear trends with inflection

Goodness of Fit — R²

The coefficient of determination R² measures how well the model fits the data:

  • R² > 0.95 — Excellent fit, high confidence in RUL estimate
  • R² = 0.80–0.95 — Good fit, moderate confidence
  • R² < 0.80 — Poor fit, consider a different model or more data

Practical Example

Example — Bearing Vibration Degradation

Given: Vibration readings at days 0, 30, 60, 90, 120, 150 are: 1.2, 1.8, 2.5, 3.4, 4.1, 5.0 mm/s. Alarm threshold = 7.1 mm/s.

Linear fit: y = 1.14 + 0.0253·t → threshold at t ≈ 236 days → RUL ≈ 86 days from last measurement.

Exponential fit may give a shorter RUL if degradation is accelerating.

⚠️ Note: Prognostic estimates depend heavily on data quality and the assumption that the degradation mechanism remains unchanged. Always combine with engineering judgment and additional condition monitoring data.

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Based on ISO 13381-1 (Condition monitoring — Prognostics). Last updated: June 2025

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Nikolai Shelkovenko

Nikolai Shelkovenko

Nikolai Shelkovenko is a vibration analysis engineer and the founder and CEO of Vibromera. For more than 15 years he has balanced rotating equipment in the field rather than on a test bench: mulchers, industrial fans, crushers, centrifuges, shafts and spindles. That work is what the Balanset instruments grew out of — they were designed as a tool a specialist can carry to the machine and use alone, on site, not as laboratory equipment. Vibromera was founded in 2017 and has been based in Porto, Portugal, since 2023. Development, assembly and support of the Balanset line all happen here. The flagship instrument is the Balanset-1A, a portable analyser for single- and two-plane balancing and for vibration diagnostics. Nikolai is personally involved in customer support, in working through difficult balancing cases and in the development of the software. He works with customers worldwide, in any language.

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