retrieves the lifetime distribution for model.
gives a list of components in ReliabilityDistribution or FailureDistribution.
gives a list of translation rules for components.
- The model can be a SystemModel object, a full model name string or a shortened model name accepted by SystemModel.
- 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. SystemModelReliability automatically computes the distribution for the system reliability from its component reliability distributions.
- The output from SystemModelReliability can be used with functions such as:
Mean mean time to failure of system SurvivalFunction probability of system functioning at time t HazardFunction failure rate at time t, given that the system is working RandomVariate simulate system lifetimes
- The reliability information for models can be edited in SystemModeler.
- SystemModelReliability[model] may return the system reliability using ReliabilityDistribution or FailureDistribution.
Examplesopen allclose all
Basic Examples (3)
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:
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:
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. A 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:
Wolfram Research (2018), SystemModelReliability, Wolfram Language function, https://reference.wolfram.com/language/ref/SystemModelReliability.html.
Wolfram Language. 2018. "SystemModelReliability." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/SystemModelReliability.html.
Wolfram Language. (2018). SystemModelReliability. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/SystemModelReliability.html