femto

The FEMTO (PRONOSTIA) Bearing dataset is a collection of run-to-failure experiments on bearings. Three different operation conditions were used, resulting in three sub-datasets. Sub-dataset 1 and 2 contain two training runs and five test runs, while sub-dataset 3 contains only one test run. It was part of the 2012 IEEE Prognostics Challenge.

FemtoReader

Bases: AbstractReader

This reader represents the FEMTO (PRONOSTIA) Bearing dataset. Each of its three sub-datasets contains a training and a test split. By default, the reader constructs a validation split for sub-datasets 1 and 2 each by taking the first run of the test split. For sub-dataset 3, the second training run is used for validation because only one test run is available. The remaining training data is denoted as the development split. This run to split assignment can be overridden by setting run_split_dist.

The features contain windows with three channels. Only the two acceleration channels are used because the test runs are missing the temperature channel. These features are standardized to zero mean and one standard deviation. The scaler is fitted on the development data.

Examples:

Default splits:

>>> import rul_datasets
>>> fd1 = rul_datasets.reader.FemtoReader(fd=1)
>>> fd1.prepare_data()
>>> features, labels = fd1.load_split("dev")
>>> features[0].shape
(2803, 2560, 2)

Custom splits:

>>> import rul_datasets
>>> splits = {"dev": [5], "val": [4], "test": [3]}
>>> fd1 = rul_datasets.reader.FemtoReader(fd=1, run_split_dist=splits)
>>> fd1.prepare_data()
>>> features, labels = fd1.load_split("dev")
>>> features[0].shape
(2463, 2560, 2)

Set first-time-to-predict:

>>> import rul_datasets
>>> fttp = [10, 20, 30, 40, 50]
>>> fd1 = rul_datasets.reader.FemtoReader(fd=1, first_time_to_predict=fttp)
>>> fd1.prepare_data()
>>> features, labels = fd1.load_split("dev")
>>> labels[0][:15]
array([2793., 2793., 2793., 2793., 2793., 2793., 2793., 2793., 2793.,
       2793., 2793., 2792., 2791., 2790., 2789.])

fds: List[int] property

Indices of available sub-datasets.

__init__(fd, window_size=None, max_rul=None, percent_broken=None, percent_fail_runs=None, truncate_val=False, run_split_dist=None, first_time_to_predict=None, norm_rul=False, truncate_degraded_only=False)

Create a new FEMTO reader for one of the sub-datasets. By default, the RUL values are not capped. The default window size is 2560.

Use first_time_to_predict to set an individual RUL inflection point for each run. It should be a list with an integer index for each run. The index is the time step after which RUL declines. Before the time step it stays constant. The norm_rul argument can then be used to scale the RUL of each run between zero and one.

For more information about using readers refer to the reader module page.

Parameters:

Name	Type	Description	Default
fd	int	Index of the selected sub-dataset	required
window_size	Optional[int]	Size of the sliding window. Defaults to 2560.	None
max_rul	Optional[int]	Maximum RUL value of targets.	None
percent_broken	Optional[float]	The maximum relative degradation per time series.	None
percent_fail_runs	Optional[Union[float, List[int]]]	The percentage or index list of available time series.	None
truncate_val	bool	Truncate the validation data with percent_broken, too.	False
run_split_dist	Optional[Dict[str, List[int]]]	Dictionary that assigns each run idx to each split.	None
first_time_to_predict	Optional[List[int]]	The time step for each time series before which RUL is constant.	None
norm_rul	bool	Normalize RUL between zero and one.	False
truncate_degraded_only	bool	Only truncate the degraded part of the data (< max RUL).	False

prepare_data()

Prepare the FEMTO dataset. This function needs to be called before using the dataset and each custom split for the first time.

The dataset is downloaded from a custom mirror and extracted into the data root directory. The whole dataset is converted from CSV files to NPY files to speed up loading it from disk. Afterwards, a scaler is fit on the development features. Previously completed steps are skipped.