equipment-maintenance

#1 Self hosted CMMS web & mobile application that allows you to manage enterprise maintenance for free - Computerized Maintenance Management System

Python-based CNC fault diagnosis system with knowledge graphs, NLP, case retrieval, Q&A, and maintenance feedback workflows.

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.

Autonomous industrial AI agent for closed-loop equipment operations and supply-chain coordination. Features RAG diagnostics, tool calling, multi-turn interaction, and strict data compliance.

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.

UptimeHealth — independent third-party profile of a public API surface, by API Evangelist. UptimeHealth is a healthcare operations platform for managing medical equipment service, compliance documentation, and facility maintenance across dental, medical, and veterinary practices from a single dashboard.

Aquant.io — independent third-party profile of a public API surface, by API Evangelist. Aquant is an agentic AI platform purpose-built for organizations that build, sell, and service complex equipment. Founded in 2016 and headquartered in New York, the company turns service content — equipment manuals, service histories, parts and warranty data, an