predictivemaintenance

End-to-end AI-powered predictive maintenance system using machine learning and Streamlit dashboard

The Predictive Maintenance API is an advanced AI-powered solution for monitoring, analyzing, and predicting failures in industrial equipment. By integrating IoT sensors and ERP systems, this API processes real-time data to detect anomalies and predict failures before they happen, reducing maintenance costs and downtime.

IIoT Predictive Maintenance system using Isolation Forest ML to detect equipment anomalies from sensor data — Python, scikit-learn

Machine Learning project for predicting industrial machine failures using Flask web application.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. AI for Predictive Maintenance in Manufacturing predicts equipment failures using sensor and historical data, enabling timely maintenance, reducing downtime, and improving overall operational efficiency.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Deep Learning for Predictive Maintenance leverages neural networks to analyze sensor and operational data, predict failures early, reduce downtime, and optimize maintenance costs.

A Machine Learning pipeline using Random Forest to predict mechanical failures in oil and gas infrastructure. Features automated noise reduction and signal processing for high-precision anomaly detection.

Predictive maintenance project using NASA C-MAPSS sensor data to predict remaining useful life and near-term engine failure. Project uses models such as XGBoost and regression techniques.

This project applies predictive maintenance on NASA Turbojet Engine Dataset, aiming to forecast maintenance needs by analyzing historical data, thereby minimizing downtime.