Proportional hazard survival model for estimating remaining useful life

Use `covariateSurvivalModel`

to estimate the remaining
useful life (RUL) of a component using a proportional hazard survival model. This model
describes the survival probability of a test component using historical information
about the life span of components and associated covariates. Covariates are
environmental or explanatory variables, such as the component manufacturer or operating
conditions. Covariate survival models are useful when the only data you have is the
failure times and associated covariates for an ensemble of similar components, such as
multiple machines manufactured to the same specifications. For more information on the
survival model, see Proportional Hazard Survival Model.

To configure a `covariateSurvivalModel`

object for a specific type
of component, use `fit`

,
which estimates model coefficients using a collection of failure-time data and
associated covariates. After you configure the parameters of your covariate survival
model, you can then predict the remaining useful life of similar components using
`predictRUL`

. For a basic example illustrating RUL prediction, see
Update RUL Prediction as Data Arrives.

If you have only life span measurements and do not have covariate information, use a
`reliabilitySurvivalModel`

.

For general information on predicting remaining useful life, see Models for Predicting Remaining Useful Life.

creates
a covariate survival model for estimating RUL and initializes the model with
default settings.`mdl`

= covariateSurvivalModel

creates a covariate survival model and initializes the model parameters using an
existing `mdl`

= covariateSurvivalModel(`initModel`

)`covariateSurvivalModel`

object
`initModel`

.

specifies user-settable model properties using name-value pairs. For example,
`mdl`

= covariateSurvivalModel(___,`Name,Value`

)`covariateSurvivalModel('LifeTimeUnit',"days")`

creates a
covariate survival model with that uses days as a lifetime unit. You can specify
multiple name-value pairs. Enclose each property name in quotes.

`predictRUL` | Estimate remaining useful life for a test component |

`fit` | Estimate parameters of remaining useful life model using historical data |

`plot` | Plot survivor function for covariate survival remaining useful life model |

- Update RUL Prediction as Data Arrives
- RUL Estimation Using RUL Estimator Models
- Cox Proportional Hazards Model (Statistics and Machine Learning Toolbox)