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

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.

An investigation for integration of deep learning and digital twins towards Construction 4.0

“…The potential benefits of ML in construction are increasingly well known through predictive analytics applications and ML capabilities have continued to grow, especially in areas such as DL (Venkatasubramanian, 2021). The predictive model to predict failures is based on a DL model and can be reused on similar installations in different facilities by using transfer learning, which can allow one to cut the development cost and reduce the implementation time (Bouabdallaoui et al, 2021). There are DL approaches to create solutions to construction problems like generative design, cash flow prediction and project risk analysis.…”

“…However, the availability of data, due to the widespread use of predictive analytics applications, the potential advantages of DL in construction are becoming more widely recognized, and the capability of DL has continued to develop, particularly in areas such as DT. By utilizing learning algorithm, the predictive model to forecast failures may be reused on identical installations in multiple facilities, providing for a reduction in development costs and a decrease in implementation time (Bouabdallaoui et al, 2021). For construction challenges such as generative design, cash flow prediction and project risk analysis, there are DL techniques that may be used to generate solutions.…”

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

“…Nevertheless, as a purely data-driven approach, the framework did not have a human-machine interaction element. Bouabdallaoui et al (2021) proposed a machine learning-based framework for predictive maintenance of building facilities. The proposed framework architecture consisted of multiple chronological procedures including data collection and pre-processing, model development, model deployment, and feedback and model improvement.…”

IoT Open-Source Architecture for the Maintenance of Building Facilities

“…Preventive maintenance involves system inspection and control at fixed intervals to lessen the likelihood of it failing unexpectedly [9]. Predictive maintenance, through sensors and machine learning algorithms, allows detection of abnormal behaviors well before accidents happen [10]. Presently, this is mainly applied in manufacturing industries [11]: out-of-range data [12] are identified, analyzed and intervention procedures are implemented to restore the system's correct behavior.…”