This dataset reflects real predictive maintenance encountered in the industry with measurements from real equipments. The features description is taken directly from the dataset source.
Dataset Link: https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset
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 a power of 2860 W, overlaid with a normally distributed noise
Torque [Nm]: Torque values are normally distributed around 40 Nm with a 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.
Machine failure : Failure or No failure (to perform binary classification)
Failure Type: Type of failure (to perform multiclass classification).
The machine failure type are as mentioned below
Tool wear failure (TWF): The tool will be replaced on failure
Heat dissipation failure (HDF): Heat dissipation causes a process failure
Power failure (PWF): The product of torque and rotational speed (in rad/s) equals the power required for the process.
Overstrain failure (OSF)
Random failures (RNF): Each process has a chance of 0,1 % to fail regardless of its process parameters.
UID: Unique identifier ranging from 1 to 10000
Product ID: Consists of letters L, M, or H for low (50% of all products), medium (30%) and high (20%) as product quality variants and a variant-specific serial number