Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

Vibration analysis tool, Signal processing tool

Code basis for the paper "Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles" (2023)

An AI-driven Prognostics and Health Management (PHM) system for predictive maintenance. This project features a FastAPI for real-time condition monitoring, advanced models for fault detection, and Remaining Useful Life (RUL) prediction. It integrates digital twin concepts and Explainable AI (XAI) for transparent and reliable industrial asset manage

AI-Powered Predictive Maintenance & Fault Diagnosis through Model Context Protocol. An open-source framework for integrating Large Language Models with predictive maintenance and fault diagnosis workflows.

Course project on Internet of Things and Signal Analysis for Condition Monitoring

Python package to simplify rotary machines vibration-based analysis

Implementation of a CM system for a wind turbine and wind farm

Real-time machine status update using MTConnect and MQTT protocols

Advanced Condition Monitoring and Remaining Useful Life Prediction Framework using Deep Learning for Industrial Equipment Prognosis and Predictive Maintenance

End-to-end demo of a modern data stack for vehicle telemetry, manufacturing traceability, and lifecycle analytics.

🛰 Monitor rocket system health with point anomaly detection on time series data using simple detectors and evaluate performance metrics.

A deep learning–based RPM estimator based on spectral features extracted from rotating machine vibration signals, implemented as a PyTorch custom model and deployed as a Flask API.

AI-powered maintenance scheduling system using Deep Q-Learning

This repository contains a small, focused **AI-assisted health monitoring** demo for a **liquid-propellant rocket engine**.

This work is from my master thesis: Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss.