Multi-Class Industrial Machine Failure Prediction Using a Balanced Fully Connected Neural Network

Description:

A predictive maintenance dataset for identifying industrial machine failures from operational and process parameters. It supports multi-class failure prediction and development of intelligent machine-health monitoring models.

Input:

Air Temperature, Process Temperature, Rotational Speed, Torque, Tool Wear, and related machine operating parameters.

Output:

Multi-class Machine Failure Type.

Tags:

#PredictiveMaintenance #MachineFailure #IndustrialAI #FaultPrediction #NeuralNetwork #DeepLearning #MachineLearning

References:

1. Allam, I. M. A. (2026, March). Multinomial Logistic Regression in C: A Comprehensive Implementation for Multi-Class Classification.

2. Junaid, A., Iqbal, A., Khan, A., Husnain, G., Ahmad, A. R., & Al-Naeem, M. (2026). Engine failure prediction on large-scale cmapss data using hybrid feature selection and imbalance-aware learning. Computers, Materials & Continua, 87(1), 61.