Datasets for Predictive Maintenance

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM

Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components

This work proposes a joint-probabilistic model between the remaining life and inspection observations, which is then used to perform prognostics on currently installed assets. At every new observation, the forward-looking belief on the asset's remaining life is Bayesian updated, granting dynamic estimations on its failure probability. Consequent…

Learn how to use high-frequency algorithms offered under SAS Analytics for IoT for Condition Based Maintenance

Python package to simplify rotary machines vibration-based analysis

This repository provides code for the paper "Vipul Bansal, Yong Chen, Shiyu Zhou, Component-Wise Markov Decision Process for Solving Condition Based Maintenance of Large Multi-Component Systems with Economic Dependence"

Quantile Regression DQN implementation for bridge fleet maintenance optimization using Markov Decision Process. Migrated from C51 distributional RL (v0.8) with 200 quantiles and Huber loss. Features: Dueling architecture, Noisy Networks, PER, N-step learning. All 6 maintenance actions show positive returns with 68-78% VaR improvement.

Multi-Equipment CBM (Condition-Based Maintenance) optimization using Deep Q-Learning with cost leveling and scenario comparison. Advanced RL system with QR-DQN, N-step learning, and parallel environments for HVAC equipment predictive maintenance.

A comprehensive reinforcement learning system for pump equipment condition-based maintenance using DQN (Deep Q-Network) with quantile regression and aging factor integration.

This system analyzes bridge repair method recommendation reports generated by AI agents and visualizes the decision-making pathway from damage → deterioration factors → repair methods as a Decision Tree. It aims to "make the thought process visible."

My Personal Project

Aging-Aware Condition-Based Maintenance System using Deep Q-Learning. This project implements a Condition-Based Maintenance (CBM) system that considers equipment aging (deterioration) using Deep Q-Learning (DQN).

A Reinforcement Learning MVP (Minimum Viable Product) for Condition-Based Maintenance (CBM) using industrial equipment temperature sensor data. This project implements a sophisticated QR-DQN (Quantile Regression Deep Q-Network) agent to learn optimal maintenance policies balancing risk mitigation and cost minimization.

Repozytorium zawiera kod stworzonej w ramach projektu System diagnostyczny układu napędowego studenckiej lokomotywy PUTrain w skali 1:5,5 w programie Studenckie Koła Naukowe Tworzą Innowacje Ministerstwa Nauki i Szkolnictwa Wyższego

Development of an tool to guide maintenance decision making for a variety of machines. Assignment during the Asset Management course at the University of Groningen

Multi-Equipment CBM system using QR-DQN with advanced probability distribution analysis. Coordinated maintenance decision-making for 4 industrial equipment units with realistic anomaly rates (1.9-2.2%), comprehensive risk analysis (VaR/CVaR), and 51-quantile distribution visualization.