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Related Experiment Video

Updated: Nov 18, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Published on: January 5, 2024

Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach.

Yassine Bouabdallaoui¹, Zoubeir Lafhaj¹, Pascal Yim²

¹Laboratoire de Mécanique Multiphysique Multiéchelle, LaMcube, UMR 9013, Centrale Lille, CNRS, Université de Lille, F-59000 Lille, France.

Sensors (Basel, Switzerland)

|February 6, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning framework for predictive maintenance in buildings, aiming to reduce energy waste. The framework successfully predicted failures in a sports facility

Keywords:

HVAC IoT autoencoders buildings data machine learning predictive maintenance

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Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Building Operations & Maintenance
Artificial Intelligence in Engineering
Sustainable Building Management

Background:

Building maintenance is often inefficient, leading to significant energy waste.
Information and Communication Technology (ICT) solutions have improved management but not efficiency.
Current practices lack proactive strategies for preventing equipment failures.

Purpose of the Study:

To propose a predictive maintenance framework for building installations.
To provide guidelines for implementing machine learning-based predictive maintenance.
To demonstrate the framework's efficacy in a real-world building setting.

Main Methods:

Developed a five-step framework: data collection, processing, model development, fault notification, and model improvement.
Utilized Internet of Things (IoT) devices and Building Automation Systems (BAS) for data collection.
Employed a deep learning model for failure prediction in Heating, Ventilation, and Air Conditioning (HVAC) systems.

Main Results:

The case study demonstrated the framework's potential for accurate failure prediction.
Identified obstacles including data availability and feedback collection challenges.
Validated the predictive capabilities of the deep learning model for HVAC installations.

Conclusions:

The proposed framework offers a viable approach to implementing predictive maintenance in buildings.
Guidelines are provided for scientists and practitioners to adopt these advanced maintenance strategies.
Further work is needed to address data-related barriers for widespread implementation.