The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TwinnableAgent C-MAPSS Processed Parquet Data
This dataset hosts the processed NASA C-MAPSS turbofan degradation files used by TwinnableAgent. It includes FD001 through FD004 as parquet files plus the conversion script used to derive them from the NASA raw training text files.
Raw NASA zip and text dumps are intentionally not mirrored here. The raw source
of record is the NASA Prognostics Center of Excellence C-MAPSS Turbofan Engine
Degradation dataset, currently distributed through NASA Open Data as
CMAPSSData.zip.
Files
| File | Rows | Units | Operating conditions | Fault modes |
|---|---|---|---|---|
FD001_processed.parquet |
20,631 | 100 | 1 | 1 |
FD002_processed.parquet |
53,759 | 260 | 6 | 1 |
FD003_processed.parquet |
24,720 | 100 | 1 | 2 |
FD004_processed.parquet |
61,249 | 249 | 6 | 2 |
scripts/convert_cmapss_to_parquet.py |
- | - | - | - |
Conversion
Each processed parquet is derived from the matching NASA training file:
train_FD001.txt -> FD001_processed.parquet
train_FD002.txt -> FD002_processed.parquet
train_FD003.txt -> FD003_processed.parquet
train_FD004.txt -> FD004_processed.parquet
The conversion keeps the NASA column order, assigns explicit column names, and adds a per-row remaining-useful-life label:
rul = max(cycle for unit) - cycle
The conversion command used by TwinnableAgent is:
python scripts/convert_cmapss_to_parquet.py --datasets FD001 FD002 FD003 FD004
Schema
All four parquet files use the same schema:
| Column | Type | Meaning |
|---|---|---|
unit |
integer | Engine trajectory identifier. |
cycle |
integer | Time-cycle index within the trajectory. |
op1, op2, op3 |
float | NASA operating setting columns. |
s1 through s21 |
float | NASA sensor channels. Sensor units are anonymized by C-MAPSS. |
rul |
integer | Remaining useful life in cycles, derived during conversion. |
Operating-condition clusters
TwinnableAgent PAL resolves FD002 and FD004 into six operating-condition groups by rounding the three operating settings to the nearest integer and assigning a stable label by sorted tuple order.
FD002 mapping:
| Cluster | Rounded (op1, op2, op3) |
Rows | Units represented |
|---|---|---|---|
op_cluster_0 |
(0, 0, 100) |
8,044 | 260 |
op_cluster_1 |
(10, 0, 100) |
8,096 | 260 |
op_cluster_2 |
(20, 1, 100) |
8,122 | 260 |
op_cluster_3 |
(25, 1, 60) |
8,002 | 260 |
op_cluster_4 |
(35, 1, 100) |
8,037 | 260 |
op_cluster_5 |
(42, 1, 100) |
13,458 | 260 |
FD004 mapping:
| Cluster | Rounded (op1, op2, op3) |
Rows | Units represented |
|---|---|---|---|
op_cluster_0 |
(0, 0, 100) |
9,238 | 249 |
op_cluster_1 |
(10, 0, 100) |
9,224 | 249 |
op_cluster_2 |
(20, 1, 100) |
9,091 | 249 |
op_cluster_3 |
(25, 1, 60) |
9,139 | 249 |
op_cluster_4 |
(35, 1, 100) |
9,162 | 249 |
op_cluster_5 |
(42, 1, 100) |
15,395 | 249 |
Provenance note: FD004 unit count
The NASA readme describes FD004 training data as 248 trajectories, but the raw
train_FD004.txt file contains 249 distinct unit identifiers. This repository
preserves the raw file as authoritative and reports FD004 as 249 units.
Intended use
These files support TwinnableAgent reproducibility, PAL adapter validation, cross-dataset predictive-maintenance experiments, and C-MAPSS RUL benchmark work. They are processed training trajectories, not raw NASA archives.
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