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Estimate parameters of remaining useful life model using historical data

The `fit`

function estimates the parameters of a remaining
useful life (RUL) prediction model using historical data regarding the health of an
ensemble of similar components, such as multiple machines manufactured to the same
specifications. Depending on the type of model, you specify the historical health data
as a collection of lifespan measurements or degradation profiles. Once you estimate the
parameters of your model, you can then predict the remaining useful life of similar
components using the `predictRUL`

function.

Using `fit`

, you can configure the parameters for the following
types of estimation models:

Degradation models

Survival models

Similarity models

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

For general information on predicting remaining useful life using these models, see RUL Estimation Using RUL Estimator Models.

`fit(`

fits the parameters of `mdl`

,`data`

,`lifeTimeVariable`

)`mdl`

using the time variable
`lifeTimeVariable`

and sets the
`LifeTimeVariable`

property of `mdl`

.
This syntax applies only when `data`

contains:

Nontabular data

Tabular data, and

`mdl`

does not use data variables

`fit(`

fits the parameters of `mdl`

,`data`

,`lifeTimeVariable`

,`dataVariables`

)`mdl`

using the data variables in
`dataVariables`

and sets the
`DataVariables`

property of
`mdl`

.

`fit(`

specifies the censor variable for a survival model and sets the
`mdl`

,`data`

,`lifeTimeVariable`

,`dataVariables`

,`censorVariable`

)`CensorVariable`

property of `mdl`

. The
censor variable indicates which life-time measurements in
`data`

are not end-of-life values. This syntax applies
only when `mdl`

is a survival model and
`data`

contains tabular data.

`fit(`

specifies the encoded variables for a covariate survival model and sets the
`mdl`

,`data`

,`lifeTimeVariable`

,`dataVariables`

,`censorVariable`

,`encodedVariables`

)`EncodedVariables`

property of `mdl`

.
Encoded variables are usually nonnumeric categorical features that
`fit`

converts to numeric vectors before fitting. This
syntax applies only when `mdl`

is a
`covariateSurvivalModel`

object and
`data`

contains tabular data.