Document Owner - Jatin | Last Updated on - 02/04/2025

1. Introduction

1.1 Overview

HVAC (Heating, Ventilation, and Air Conditioning) systems are critical for maintaining indoor air quality and temperature control. However, unexpected failures in Air Handling Units (AHUs) and Variable Air Volume (VAV) systems lead to energy inefficiencies, high repair costs, and operations downtime. This project aims to leverage AI-powered fault prediction to detect issues before they occur, reducing maintenance costs and improving system longevity.

1.2 Understanding the Product Domain

🔹 What is a BMS (Building Management System)?

A BMS (Building Management System) is a centralized control system that monitors and manages mechanical and electrical equipment in a building, including HVAC (Heating, Ventilation, and Air Conditioning), lighting, power systems, fire alarms, and security systems.

🔹 What is a HVAC System?

HVAC (Heating, Ventilation, and Air Conditioning) systems regulate temperature, humidity, and air quality in a building. Key components:

• AHU (Air Handling Unit) – Circulates and conditions air.
• VAV (Variable Air Volume) Systems – Adjust airflow for efficiency.
• Sensors – Monitor temperature, pressure, power usage, and vibration to detect faults.

🔹 Why AI-Powered Fault Prediction for HVAC?

• Traditional HVAC maintenance is reactive – Failures cause high downtime and costs.
• AI-based predictive maintenance helps prevent failures before they happen.
• Energy savings & efficiency – Optimizing HVAC operations reduces power consumption.

1.3 Objectives

• Develop a predictive maintenance system for HVAC components (AHUs & VAVs).
• Utilize AI and machine learning to detect faults before failure.
• Provide real-time monitoring and alerts for maintenance teams.