This dataset reflects real predictive maintenance encountered in the industry with measurements from real equipment. The features description is taken directly from the dataset source.
Objectives:
- Finding insights by doing an Explanatory Data Analysis (EDA).
- Developing two ML models.
- One for binary classification to predict Failure or No Failure.
- One for multiclass classification to predict Type of Failure.
- Exploring some insights from the results of the models.
Challenges:
- Dealing with highly imbalanced data.
The six features are:
- Type: the quality of the product, consisting of a letter L, M, or H. Meaning low, medium, and high, respectively.
- Air temperature [K]: generated using a random walk process later normalized to a standard deviation of 2 K around 300 K.
- Process temperature [K]: generated using a random walk process normalized to a standard deviation of 1 K, added to the air temperature
plus 10 K. - Rotational speed [rpm]: calculated from power of 2860 W, overlaid with a normally distributed noise.
- Torque [Nm]: torque values are normally distributed around 40 Nm with an σ = 10 Nm and no negative values.
- Tool wear [min]: The quality variants H/M/L add 5/3/2 minutes of tool wear to the used tool in the process.
The targets are:
- Target: failure or no failure (to perform binary classification).
- Failure Type: type of failure (to perform multiclass classification).
It also includes the following information, which is not useful for building the models:
- UID: unique identifier ranging from 1 to 10000.
- ProductID: the id of the product.
Dataset source: https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification