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dataset_info:
features:
- name: engine_no
dtype: int64
- name: time_in_cycles
dtype: int64
- name: operational_setting_1
dtype: float64
- name: operational_setting_2
dtype: float64
- name: operational_setting_3
dtype: float64
- name: s_1
dtype: float64
- name: s_2
dtype: float64
- name: s_3
dtype: float64
- name: s_4
dtype: float64
- name: s_5
dtype: float64
- name: s_6
dtype: float64
- name: s_7
dtype: float64
- name: s_8
dtype: float64
- name: s_9
dtype: float64
- name: s_10
dtype: float64
- name: s_11
dtype: float64
- name: s_12
dtype: float64
- name: s_13
dtype: float64
- name: s_14
dtype: float64
- name: s_15
dtype: float64
- name: s_16
dtype: float64
- name: s_17
dtype: float64
- name: s_18
dtype: float64
- name: s_19
dtype: float64
- name: s_20
dtype: float64
- name: s_21
dtype: float64
- name: rul
dtype: int64
- name: cluster
dtype: int32
- name: condition_ind
dtype: float64
splits:
- name: train
num_bytes: 12257052
num_examples: 53759
- name: test
num_bytes: 7749948
num_examples: 33991
download_size: 9762754
dataset_size: 20007000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
Feature Engineered version of [this](https://huggingface.co/datasets/uncledecart/fed-turbofan) dataset. Key differences:
- GBDT feature selection (selected on train data, target is rul, threshold is 0.01, lightgbm.LGBMRegressor is used)
- Sensor-wise z_score and min_max normalisation (effectively just min_max, applied z_score as in [this](https://www.researchgate.net/publication/355164116_Data-Driven_Deep_Learning-Based_Attention_Mechanism_for_Remaining_Useful_Life_Prediction_Case_Study_Application_to_Turbofan_Engine_Analysis) paper) |