Predictive Maintenance for Industrial Equipment using AI
- Version
- Download 26
- File Size 222.84 KB
- File Count 1
- Create Date 23 May 2025
- Last Updated 23 May 2025
Predictive Maintenance for Industrial Equipment using AI
Authors:
Mr. Somagani Venkatesh*1, Gujjula Sree Saran*2, Jella Sanjay*3, Guttala Anumol*4
*1Assistant Professor of Department of CSE (AI & ML) of ACE Engineering College, Hyderabad, India.
*2,3,4 Department CSE (AI & ML) of ACE Engineering College, Hyderabad, India.
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.
Download