Most line models simulate what machines are designed to do. ReliaSim’s interrupts model what actually happens, the random failures and repairs that override nominal behavior. ReliaStats® is the tool for setting those interrupts up. It’s desktop software for Mac and Windows that runs entirely on your own machine.
Every interrupt captures a real-world disruption, a random event that overrides a machine’s nominal behavior. Each one is defined by two distributions: how long a node runs before the event occurs (TTF) and how long recovery takes (TTR). If these are wrong, the simulation will be wrong too.
Time to failure is how long a node operates before an interrupt occurs. That covers random breakdowns, scheduled downtime, wear-out and volume-based triggers.
Time to repair is how long a node is unavailable once interrupted, for maintenance, resets, refills or operator response. TTF and TTR together are the machine’s reliability profile.
Design distributions from scratch, explore fitted parameters, or compare versions side by side. You don’t need a statistician, and nothing is uploaded.
“Traditional methods of parameterizing interrupts require dedicated time and attention from an expert to manually manipulate the tape and use their knowledge to distill line event data into cause groups, each with unique TTF and TTR distributions. The process is time-consuming, expensive, and requires an expert.”ReliaSim documentation on interrupt parameterization
The fitting engine turns raw line event data (LEDS) into interrupt distributions, fitted per failure mode, with goodness-of-fit output you can check. A raw event tape goes in, and TTF and TTR distributions mapped to cause groups come out, ready for ReliaSim.
Input
One row per stop from a historian or downtime system: where, why, when, or the uptime and downtime in minutes.
Engine
Finds the cause groups automatically, fits all eight distribution types to each, and ranks them with Kolmogorov-Smirnov and Anderson-Darling tests.
Output
A TTF and TTR pair for every interrupt, exported as a file ReliaSim imports directly, then checked against the line’s own history.
The loop closes when ReliaSim’s output comes back to ReliaStats and is checked against the same history, interrupt by interrupt.
Explore, design, view, compare and validate interrupt distributions in the browser, and fit them automatically in the ReliaStats desktop app. Explorer, Quick Start and Designer are free. Viewer, Comparison, Validation and desktop fitting need a ReliaStats subscription.
Free
The guided way in. Pick an equipment failure pattern and ReliaStats sets a sensible TTF and TTR pair for you, with the availability and stops per shift it implies.
Free
Build a TTF and TTR pair from scratch. See PDF, CDF and survival curves, a p5 to p95 box-and-whisker, a working and failed timeline over a shift, and the availability the pair implies.
Free with sign-in
Pareto-rank every failure cause at once and overlay their survival curves R(t), straight from .xlsx or .csv stop logs. It runs locally, so your data never leaves your machine.
Subscription · desktop app
Import raw historian event logs. ReliaStats identifies cause groups, fits and ranks all eight distribution types with K-S and Anderson-Darling tests, and exports a ReliaSim-ready file.
Subscription
Load any ReliaSim interrupt file and click through every interrupt’s uptime and downtime distributions, a fast audit of a full model’s parameters.
Subscription
Two interrupt files side by side, with every numeric difference shown, rows marked by size of change and the curves overlaid.
Subscription
Source against simulated availability, interrupt by interrupt, with 95% prediction-interval and 99% confidence-interval bands. Points on the diagonal match history, and points outside the bands show where to refine.
ReliaStats owns the data step. It turns raw event history into interrupt distributions, then closes the loop by checking the simulation’s results against the same history.
Define the production line in ReliaSim and parameterize each machine’s interrupt behavior from raw historian event data.
This is the ReliaStats step
Check the simulation against the line’s measured performance. A published food-plant model rebuilt in ReliaSim matched measured OEE within 1% (case study).
Run what-if scenarios and commit capital where the validated model says it belongs, without touching the line. The ReliaSim method covers all three steps.
Every distribution ReliaSim supports, with parameters exactly as ReliaSim stores them, including real-space mean and standard deviation for LogNormal.
λ scale, κ shape, γ location
Shape κ < 1 is infant mortality, κ = 1 is random, κ > 1 is wear-out. The most versatile reliability distribution.
Wear-out and random failures
mean x̄, std dev s
Right-skewed: most values small, with occasional large outliers. A good fit for variable repair times.
Repair times, compounded delays
μ mean, σ std dev
Symmetric around the mean. Use it carefully for TTF, since it allows negative values unless constrained.
Consistent human-driven processes
λ rate, mean = 1/λ
Memoryless: the chance of failure doesn’t grow with age. A constant failure rate.
Random, independent failures
min, max
Equal probability across a range. Useful when a value can fall anywhere in a range and none is preferred.
Limited data, bounded range
min, max, mode
A simple three-point estimate, easy to explain when only expert estimates are available.
Expert estimates, quick modeling
γ, δ, ξ, λ
A flexible four-parameter family that handles skewed and heavy-tailed data simpler distributions can’t capture.
Complex empirical data
x value
Deterministic: it always returns the same value. Models predictable scheduled events with no variance.
Shift changes, planned maintenance
Every component follows a failure-rate pattern over its life. Knowing which phase your equipment is in tells you which distribution to use, and which fix to reach for.
Weibull (κ < 1)
Defects, installation errors and manufacturing variation cause early failures that decrease over time. The component is most likely to fail soon after it goes into service.
In the Designer, a Weibull with κ < 1 captures this. The hazard is highest at t = 0 and falls as the weak units fail out.
Burn-in testing, strict incoming QA, early replacement schedules
Exponential, Weibull (κ = 1)
Failures occur independently of age: external shocks, operator error, voltage spikes. The failure rate is constant, so knowing how long a component has run tells you nothing about when it will fail next.
This is the useful-life phase, and the memoryless Exponential distribution is the standard model.
Redundancy, condition monitoring, failure mode analysis
Weibull (κ > 1), LogNormal
Fatigue, corrosion, erosion and material degradation make the failure rate rise with age. Failures become more predictable, and more preventable, near end of life.
Weibull with κ > 1 fits wear-out well. κ of 2 to 3 is common for mechanical fatigue, and a higher κ means failures cluster more tightly.
Scheduled replacement, life-limit programs
Set any TTF and TTR pair and see the curves, percentiles and availability move. It runs in your browser with no account, and you can download it to run offline.
Launch the Designer Book a live walkthrough