Fit your first life model from a CSV

Everything else in Reliafy is built on a fitted life model, so this is the place to start. The input is modest: a column of numbers saying how long things lasted. The output is a distribution you can ask questions of — what fraction survive to 5,000 hours, when will 10% have failed, what's the mean life, and is this thing wearing out or failing randomly.

What your data needs to look like

One row per unit, and a column of times. That's the minimum:

hours
312
420
506

The units can be anything — hours, cycles, kilometres, months — as long as you're consistent. What matters far more is the second column most real datasets need: censoring.

A censored unit is one that hasn't failed yet. If you pulled 30 pumps from service and 18 had failed while 12 were still running fine when you stopped looking, those 12 are not "failures at the time I stopped looking" — they're units that survived at least that long. Throwing them away, or worse, treating them as failures, will make your equipment look considerably worse than it is. Reliafy handles them properly if you tell it which is which, conventionally with a 0 for a failure and 1 for a still-running unit:

hours,censored
312,0
420,0
2000,1

If you have interval data (the unit failed somewhere between two inspections), or counts, or truncation, those are supported too — but exact times plus censoring covers most real cases.

Loading it in

Go to Modelling → New model and choose Fit to data. You can drop a CSV straight onto the page, or pick a dataset you've already uploaded — datasets are reusable, so once a CSV is in Reliafy you can fit as many models to it as you like without re-uploading.

If your file has no header row, tick No header row when you upload it and Reliafy will name the columns col 1, col 2, … so the first row stays data rather than being eaten as titles.

Mapping the columns

Next you tell Reliafy what each column means. The mapping uses the standard survival-analysis letters, and you only need the ones your data has:

• x — the failure/censoring time. Required.
• c — the censoring flag.
• n — a count, if one row represents several identical units.
• xl / xr — lower and upper bounds, for interval-censored data (use these instead of x).
• tl / tr — left and right truncation.

Set the unit here too. It's optional, but it flows through to every chart and result afterwards, and it stops you comparing a model in hours against one in cycles later on.

Choosing a distribution

Then pick the model. If you don't know which distribution you want — and often you genuinely don't — choose best fit and Reliafy will fit every candidate and keep the one with the lowest AIC, showing you the full ranking so you can see how close the runners-up were.

If you do want to choose: Weibull is the workhorse and handles wear-out, infant mortality and random failure through its shape parameter. Exponential assumes a constant failure rate — simple, and right more rarely than people assume. Lognormal suits repair times and fatigue. There's also a full set of discrete distributions for cycles-to-failure data, non-parametric estimators (Kaplan-Meier and friends) when you don't want to assume a shape at all, and regression models when failure depends on covariates like temperature or load.

Reading the fit

The result page gives you the fitted parameters with confidence intervals, a probability plot, goodness-of-fit statistics, and a calculator.

The probability plot is the one to look at first. It puts your data on paper where a good fit is a straight line. If the points track the line, the distribution is a reasonable description of your data. If they curve away systematically, or fall into two distinct groups, that's telling you something real — often that you have two failure modes mixed together and would be better off splitting the data.

For a Weibull fit, β (beta) is the number worth understanding:

• β < 1 — failures are decreasing over time. Infant mortality: bad installs, manufacturing defects, teething problems. Replacing on a schedule makes things worse, because new units are the risky ones.
• β ≈ 1 — a constant failure rate. Failures are random, age tells you nothing, and scheduled replacement buys you nothing either.
• β > 1 — wear-out. Risk rises with age, and preventive replacement starts to make sense.

That single parameter often changes the maintenance decision more than anything else on the page.

The calculator lets you ask the model direct questions: reliability at a given time, the time by which a given fraction will have failed (B10, B50), and the mean life — all with confidence bounds.

Where to go next

Save the model and it becomes available everywhere else: as a block in a reliability block diagram, as the input to an optimal replacement interval, and as a basis for fleet forecasting.

For the theory behind the fit — how censoring is handled in the likelihood, what the parameters mean, and how to read the plots — see the Learn article on Weibull analysis.