Baselight

Balanced Predictive Maintenance Dataset

Addressing Class Imbalance in Predictive Maintenance

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset

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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.

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

Tables

Cleaned Data

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.cleaned_data
  • 72.41 kB
  • 10,000 rows
  • 7 columns
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CREATE TABLE cleaned_data (
  "type" BIGINT,
  "air_temperature_k" DOUBLE  -- Air Temperature [K],
  "process_temperature_k" DOUBLE  -- Process Temperature [K],
  "rotational_speed_rpm" DOUBLE  -- Rotational Speed [rpm],
  "torque_nm" DOUBLE  -- Torque [Nm],
  "tool_wear_min" DOUBLE  -- Tool Wear [min],
  "reason_of_failure" BIGINT
);

X Ctgan

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_ctgan
  • 4.19 MB
  • 87,458 rows
  • 6 columns
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CREATE TABLE x_ctgan (
  "type" BIGINT,
  "air_temperature_k" DOUBLE  -- Air Temperature [K],
  "process_temperature_k" DOUBLE  -- Process Temperature [K],
  "rotational_speed_rpm" DOUBLE  -- Rotational Speed [rpm],
  "torque_nm" DOUBLE  -- Torque [Nm],
  "tool_wear_min" DOUBLE  -- Tool Wear [min]
);

X Smote

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_smote
  • 2.07 MB
  • 47,229 rows
  • 6 columns
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CREATE TABLE x_smote (
  "type" BIGINT,
  "air_temperature_k" DOUBLE  -- Air Temperature [K],
  "process_temperature_k" DOUBLE  -- Process Temperature [K],
  "rotational_speed_rpm" DOUBLE  -- Rotational Speed [rpm],
  "torque_nm" DOUBLE  -- Torque [Nm],
  "tool_wear_min" DOUBLE  -- Tool Wear [min]
);

X Test

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_test
  • 33.88 kB
  • 3,000 rows
  • 6 columns
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CREATE TABLE x_test (
  "type" BIGINT,
  "air_temperature_k" DOUBLE  -- Air Temperature [K],
  "process_temperature_k" DOUBLE  -- Process Temperature [K],
  "rotational_speed_rpm" DOUBLE  -- Rotational Speed [rpm],
  "torque_nm" DOUBLE  -- Torque [Nm],
  "tool_wear_min" DOUBLE  -- Tool Wear [min]
);

X Train

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.x_train
  • 58.51 kB
  • 7,000 rows
  • 6 columns
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CREATE TABLE x_train (
  "type" BIGINT,
  "air_temperature_k" DOUBLE  -- Air Temperature [K],
  "process_temperature_k" DOUBLE  -- Process Temperature [K],
  "rotational_speed_rpm" DOUBLE  -- Rotational Speed [rpm],
  "torque_nm" DOUBLE  -- Torque [Nm],
  "tool_wear_min" DOUBLE  -- Tool Wear [min]
);

Y Ctgan

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_ctgan
  • 3.44 kB
  • 87,458 rows
  • 3 columns
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CREATE TABLE y_ctgan (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

Y Smote

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_smote
  • 3.44 kB
  • 47,229 rows
  • 3 columns
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CREATE TABLE y_smote (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

Y Test

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_test
  • 2.92 kB
  • 3,000 rows
  • 3 columns
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CREATE TABLE y_test (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

Y Train

@kaggle.chetansmahale_balanced_predictive_maintenance_dataset.y_train
  • 3.4 kB
  • 7,000 rows
  • 3 columns
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CREATE TABLE y_train (
  "reason" BIGINT,
  "of" VARCHAR,
  "failure" VARCHAR
);

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