license: other
task_categories:
- time-series-forecasting
- tabular-regression
pretty_name: TwinnableAgent C-MAPSS processed parquet data
tags:
- cmapss
- predictive-maintenance
- remaining-useful-life
- twinnableagent
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.