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Baseline models

BaselineModel

Predict the RUL using the mean or the median value of the duration of the dataset

Parameters:

Name Type Description Default
mode str

Method for computing the duration of the dataset. Possible values are: 'mean' and 'median'

 'mean'
Source code in ceruleo/models/baseline.py 
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class BaselineModel:
    """
    Predict the RUL using the mean or the median value of the duration of the dataset

    Parameters:
        mode: Method for computing the duration of the dataset. Possible values are: 'mean' and 'median'
    """

    def __init__(self, mode: str = "mean", RUL_threshold: Optional[float] = None):
        self.mode = mode
        self.RUL_threshold = RUL_threshold

    def fit(self, ds: Union[TransformedDataset, AbstractPDMDataset]):
        """Compute the mean or median RUL using the given dataset

        Parameters:
            ds:  Dataset from which obtain the true RUL
        """
        true = []
        for y in iterate_over_target(ds):
            y = y
            degrading_start, time = FittedLife.compute_time_feature(
                y, self.RUL_threshold
            )

            true.append(y.iloc[0] + time[degrading_start])

        if self.mode == "mean":
            self.fitted_RUL = np.mean(true)
        elif self.mode == "median":
            self.fitted_RUL = np.median(true)

    def predict(self, ds: TransformedDataset) -> np.ndarray:
        """
        Predict the whole life using the fitted values

        Parameters:
            ds: Dataset iterator from which obtain the true RUL

        Returns:
            Predicted RUL
        """
        output = []
        for y in iterate_over_target(ds):
            _, time = FittedLife.compute_time_feature(y, self.RUL_threshold)
            y_pred = np.clip(self.fitted_RUL - time, 0, self.fitted_RUL)
            output.append(y_pred)
        return np.concatenate(output)

fit(ds)

Compute the mean or median RUL using the given dataset

Parameters:

Name Type Description Default
ds Union[TransformedDataset, AbstractPDMDataset]

Dataset from which obtain the true RUL

 required
Source code in ceruleo/models/baseline.py 
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def fit(self, ds: Union[TransformedDataset, AbstractPDMDataset]):
    """Compute the mean or median RUL using the given dataset

    Parameters:
        ds:  Dataset from which obtain the true RUL
    """
    true = []
    for y in iterate_over_target(ds):
        y = y
        degrading_start, time = FittedLife.compute_time_feature(
            y, self.RUL_threshold
        )

        true.append(y.iloc[0] + time[degrading_start])

    if self.mode == "mean":
        self.fitted_RUL = np.mean(true)
    elif self.mode == "median":
        self.fitted_RUL = np.median(true)

predict(ds)

Predict the whole life using the fitted values

Parameters:

Name Type Description Default
ds TransformedDataset

Dataset iterator from which obtain the true RUL

 required

Returns:

Type Description
ndarray

Predicted RUL
Source code in ceruleo/models/baseline.py 
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def predict(self, ds: TransformedDataset) -> np.ndarray:
    """
    Predict the whole life using the fitted values

    Parameters:
        ds: Dataset iterator from which obtain the true RUL

    Returns:
        Predicted RUL
    """
    output = []
    for y in iterate_over_target(ds):
        _, time = FittedLife.compute_time_feature(y, self.RUL_threshold)
        y_pred = np.clip(self.fitted_RUL - time, 0, self.fitted_RUL)
        output.append(y_pred)
    return np.concatenate(output)

FixedValueBaselineModel

A model that predicts always the same duration for each run-to-failure cycle

Parameters:

Name Type Description Default
value float

Fixed RUL

 required
Source code in ceruleo/models/baseline.py 
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class FixedValueBaselineModel:
    """
    A model that predicts always  the same duration for each run-to-failure cycle

    Parameters:
        value: Fixed RUL
    """

    def __init__(self, *, value: float):
        self.value = value

    def fit(self, *args):
        return self

    def predict(
        self, ds: TransformedDataset, RUL_threshold: Optional[float] = None
    ) -> np.ndarray:
        """
        Predict the whole life using the fixed values

        Parameters:
            ds: Dataset iterator from which obtain the true RUL

        Returns:
            Predicted RUL
        """
        output = []
        for y in iterate_over_target(ds):
            _, time = FittedLife.compute_time_feature(y, RUL_threshold)
            y_pred = np.clip(self.value - time, 0, self.value)
            output.append(y_pred)
        return np.concatenate(output)

predict(ds, RUL_threshold=None)

Predict the whole life using the fixed values

Parameters:

Name Type Description Default
ds TransformedDataset

Dataset iterator from which obtain the true RUL

 required

Returns:

Type Description
ndarray

Predicted RUL
Source code in ceruleo/models/baseline.py 
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def predict(
    self, ds: TransformedDataset, RUL_threshold: Optional[float] = None
) -> np.ndarray:
    """
    Predict the whole life using the fixed values

    Parameters:
        ds: Dataset iterator from which obtain the true RUL

    Returns:
        Predicted RUL
    """
    output = []
    for y in iterate_over_target(ds):
        _, time = FittedLife.compute_time_feature(y, RUL_threshold)
        y_pred = np.clip(self.value - time, 0, self.value)
        output.append(y_pred)
    return np.concatenate(output)