Tracks the trend
Horizon filters the noise of the health indicator and tracks its smoothed level and rate with a Kalman filter. The noise level is tuned from the data itself.
When will a machine's health indicator reach its threshold? Horizon shows the answer as an uncertainty band compared with your planning lead time, not as one date. Your data stays in this browser.
Narration: synthetic voice (Piper, voice “Joe”, CC0 public domain). Captions are burned into the video.
Horizon filters the noise of the health indicator and tracks its smoothed level and rate with a Kalman filter. The noise level is tuned from the data itself.
Thousands of possible futures are simulated, each carrying noise and rate uncertainty. The moment each path crosses the threshold gives a distribution of crossing times.
The result is not one date: P50 is the most likely time and the P10–P90 range shows plausible uncertainty. That range is compared with your planning lead time.
Pick a scenario. All are mathematically generated examples, not real machine data.
Drop a CSV file here
One numeric column: the row number is the time. With a date column, elapsed days are used.
The pale line is measured, the dark line is the smoothed level. The shaded band is the likely path (P10–P90). Where the band meets the dashed threshold is the failure window.
Each bar counts simulated paths that cross in that time window. Colours are zones against the lead time: red before it, amber near it, green later.
The needle compares the most likely crossing (P50) with the planning lead time.
The rate is how much the indicator changes per unit of time; a rising line means degradation is speeding up. The shade is ±2 standard deviations.
Bars show the error (RMSE, lower is better) on the last 20% of the data, which each model did not see. The table lists each model's crossing estimate.
Pick a measurable quantity that moves in one direction as the machine degrades: vibration RMS, filter differential pressure, a wear measurement. It should reflect the degradation mechanism, and the threshold should be the value where the function is lost, on the same indicator.
The future is uncertain because of load, maintenance, environment and noise. A single date hides that uncertainty. The P10–P90 range carries the model's own uncertainty and the noise honestly; it does not cover effects the model does not contain, such as sudden damage.
There is no fixed number. In practice a few hundred points and a clear trend help; a short record that is only noise gives a weak forecast. Earlier failures and the trend before them are very valuable. At least 12 points are needed; the model comparison runs from 20 points.
Horizon compares the most likely crossing and the P10 value with your planning lead time. If P10 is shorter than that time, it raises an alert: a crossing is likely to come before the team can prepare. Changing the lead time changes the decision, not the event.
No. The calculation runs in your browser in a WebAssembly module written in C. Nothing is sent anywhere and it is gone when you close the page. Files you export stay with you.
Learn how RUL is defined, what each of three common approaches needs as data, and how to report P10, P50 and P90 values with a worked trend calculation.
Learn the predict and update steps, what the gain K means, and how a level-and-slope state yields a smoothed health indicator together with its rate of change.
Compute MTBF, MTTR and availability from a hypothetical record, then see why MTBF is not a lifetime and how the Weibull shape parameter β bears on scheduled replacement.
Connect sensor behavior to operating context, maintenance events and a false-alarm budget.
Let’s review your data source, your existing system and the decision you want to improve.