WSMModelReliability is being phased out in favor of SystemModelReliability, which was introduced experimentally in Version 11.3.


retrieves the lifetime distribution for mmodel.


gives a list of components in ReliabilityDistribution or FailureDistribution.


gives a list of translation rules for components.

Details and Options

  • The reliability for a system is the probability that it functions correctly at time t. The reliability for a whole system depends on the reliability of its components. WSMModelReliability automatically computes the distribution for the system reliability from its component reliability distributions.
  • The output from WSMModelReliability can be used with functions such as:
  • Meanmean time to failure of system
    SurvivalFunctionprobability of system functioning at time t
    HazardFunctionfailure rate at time t, given that the system is working
    RandomVariatesimulate system lifetimes
  • The reliability information for models can be edited in System Modeler Model Center.
  • "mmodel" refers to the fully qualified Modelica name. The shortest unique model name mmodel can be used where WSMNames["*.mmodel"] gives a unique match.
  • WSMModelReliability["mmodel"] may return the system reliability using ReliabilityDistribution or FailureDistribution.


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Basic Examples  (4)

Load Wolfram System Modeler Link:

Retrieve the lifetime distribution for a model, in this case a ReliabilityDistribution:

Retrieve the lifetime distribution for a model:

Plot the survival function to see the likelihood a system works after a certain time:

Component names from the model are shortened:

Retrieve rules that show the mapping to the original names from the model:

Scope  (3)

Compare the lifetimes between a parallel and a serial system:

Survival functions show a higher probability of functioning for the parallel system at any time:

Compute the expected lifetime for a system:

The expected lifetime is often called the mean time to failure:

Analyze how structurally important components are for system reliability:

Retrieve the original component names:

Looking at system structure, the EMF, inductor, and inertia are the most important for reliability:

Applications  (4)

Study the reliability of an electric motor:

Retrieve the lifetime distribution, where the system works if one of the resistors works:

Show the probability of survival over time:

Compute the mean time to failure:

Retrieve the original component names referenced in the ReliabilityDistribution:

Show how structurally important each component is for reliability:

Analyze how important components are for system reliability:

The mean time to failure for this model:

BirnbaumImportance shows that the EMF component is the most important for reliability:

Compare with a model where the EMF component has been improved:

The mean system lifetime has improved around 11%:

Model the reliability of an uninterruptible power supply (UPS) setup:

Power is supplied from the utility through power lines, or from a battery through an inverter:

Compute the mean time to failure (MTTF) and convert it to years:

Compare with a version of the same system with hardened components:

The MTTF for the hardened system is significantly higher:

Compute the two survival functions:

Show the survival functions for the two systems, indicating the different MTTF:

Model how failures impact system performance:

One resistor in a DC motor fails at time 20, another at time 35:

One resistor failure decreases motor speed, two failures make the motor fail completely:

Properties & Relations  (1)

Component names are changed when retrieving a system lifetime distribution:

Retrieve the translation rules applied:

Retrieve just the original names, in the same order as referenced in the system distribution: