Predictive Maintenance for Industrial Equipment using AI

TLDR

This project leverages machine learning techniques to analyze historical sensor data and predict equipment failures before they occur by implementing supervised learning models such as Random Forest and XGBoost.

Abstract

ABSTRACT: The Predictive maintenance is a critical approach to minimizing downtime and optimizing operational efficiency in industrial environments. This project leverages machine learning techniques to analyze historical sensor data and predict equipment failures before they occur. By implementing supervised learning models such as Random Forest and XGBoost, along with feature engineering techniques, the system can detect patterns indicative of potential malfunctions. The methodology integrates Python libraries such as Pandas, Scikit-learn, TensorFlow, and Matplotlib for data preprocessing, model training and visualization. This predictive maintenance system aims to reduce maintenance costs, improve reliability, and enhance overall industrial productivity.

Keywords: Data Analysis, Random Forest, XGBoost, Supervised Learning.