Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review

Abstract

Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

“…ANNs are a commonly used type of supervised machine learning for FDD in building operations [16,[142][143][144][145][146]. They simulate the structure and function of the human brain's neurons to process and transmit data.…”

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

“…One way to identify faults is to use innovative ML/DL techniques based on artificial intelligence, which are quite versatile and smart in many contexts. Possible approaches of artificial intelligence-based fault detection and diagnosis are SVMs, ANNs, naive Bayes, and many other data-driven methods and knowledge-data methods [124]. As advised by Zao et al [125], the optimal solution is a mixture of the two approaches, to take the pros of both data-driven methods and knowledge-data methods, which place a lot of trust in the diagnostic skills of experts, whose behaviour they simulate.…”

Modelling of Energy Consumption in Manufacturing: A Practical Framework to Select the Modelling Approach

“…al. in their seminal work [20] in 2005 proposed a classification scheme to categorise various model-based methods. This framework has not only become part of the standard theory of FDD, but also provided a practical means to compare the various approaches as evidenced by its relevance in the recent articles [21,22].…”