Dataset: Balanced Predictive Maintenance Dataset

About this Dataset

Balanced Predictive Maintenance Dataset

The AI4I 2020 Predictive Maintenance Dataset (Link) is imbalanced. To address the data imbalance challenge, this dataset was augmented using statistical technique - SMOTE (Synthetic Minority Oversampling Technique) and GenAI based technique - CTAGN (Conditional Tabular Generative Adversarial Network).
This dataset contains 3 directories.

First one contains the cleaned AI4I 2020 Predictive Maintenance Dataset as well as train - test splitted datasets.
Second one contains X and y of the data augmented with CTGAN
Third one contains X and y of the data augmented with SMOTE

Tables

X Smote

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_smote

1.97 MB
47229 rows
6 columns


CREATE TABLE x_smote (
  "type" BIGINT,
  "air_temperature_k" DOUBLE,
  "process_temperature_k" DOUBLE,
  "rotational_speed_rpm" DOUBLE,
  "torque_nm" DOUBLE,
  "tool_wear_min" DOUBLE
);

Y Smote

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_smote

3.36 KB
47229 rows
3 columns


CREATE TABLE y_smote (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

X Train

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_train

57.13 KB
7000 rows
6 columns


CREATE TABLE x_train (
  "type" BIGINT,
  "air_temperature_k" DOUBLE,
  "process_temperature_k" DOUBLE,
  "rotational_speed_rpm" DOUBLE,
  "torque_nm" DOUBLE,
  "tool_wear_min" DOUBLE
);

Y Test

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_test

2.85 KB
3000 rows
3 columns


CREATE TABLE y_test (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

X Test

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_test

33.08 KB
3000 rows
6 columns


CREATE TABLE x_test (
  "type" BIGINT,
  "air_temperature_k" DOUBLE,
  "process_temperature_k" DOUBLE,
  "rotational_speed_rpm" DOUBLE,
  "torque_nm" DOUBLE,
  "tool_wear_min" DOUBLE
);

Cleaned Data

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.cleaned_data

70.71 KB
10000 rows
7 columns


CREATE TABLE cleaned_data (
  "type" BIGINT,
  "air_temperature_k" DOUBLE,
  "process_temperature_k" DOUBLE,
  "rotational_speed_rpm" DOUBLE,
  "torque_nm" DOUBLE,
  "tool_wear_min" DOUBLE,
  "reason_of_failure" BIGINT
);

Y Train

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_train

3.32 KB
7000 rows
3 columns


CREATE TABLE y_train (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

X Ctgan

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_ctgan

4 MB
87458 rows
6 columns


CREATE TABLE x_ctgan (
  "type" BIGINT,
  "air_temperature_k" DOUBLE,
  "process_temperature_k" DOUBLE,
  "rotational_speed_rpm" DOUBLE,
  "torque_nm" DOUBLE,
  "tool_wear_min" DOUBLE
);

Y Ctgan

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_ctgan

3.36 KB
87458 rows
3 columns


CREATE TABLE y_ctgan (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);