Abstract
The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.
Highlights
Received: 11 December 2020In Europe, buildings are responsible for 40% of energy consumption, and approximately 28% of total direct and indirect CO2 emissions [1,2]
Facility management (FM) teams depend on real-time, accurate and comprehensive data to perform day-to-day maintenance activities and to provide accurate information to top management [4]
The budget and resources allocated for building maintenance are limited [6], and maintenance personnel argue that their budget and resources are insufficient and below their needs [7,8]
Summary
Received: 11 December 2020In Europe, buildings are responsible for 40% of energy consumption, and approximately 28% of total direct and indirect CO2 emissions [1,2]. The activities of inspecting facilities, assessing maintenance and collecting data are labour-intensive and time consuming [5]. The budget and resources allocated for building maintenance are limited [6], and maintenance personnel argue that their budget and resources are insufficient and below their needs [7,8]. This trade-off affects the quality and the relevance of the maintenance activities and inspections, which leads to poor maintenance and quality management policies in facilities
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