Abstract
The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models have been developed over the years for AFDD of complete HVAC or its sub-systems. However, HVAC complexities, which partly have roots in its close coupling nature and interrelated dependencies, mean that understanding the relationship between faults and the suitability of the techniques remains an unanswered question. The literature analysis and interactive visualization of the data collected from the past implementation of AFDD models can provide useful insight to further explore this question by applying artificial intelligence (AI). Association rule mining (ARM) is deployed by this paper, using the frequent pattern (FP) growth algorithm to generate frequent fault sets for most common HVAC faults from the body of AFDD models developed in the literature to represent the status quo. A new model is developed for common HVAC faults and the techniques most frequently used to detect and diagnose them. A recommender system is developed using the ARM model to extract knowledge from the body of knowledge of HVAC data-driven AFDD in the form of rule-sets that reflect the associations. Findings of this review paper can significantly help civil and building engineers, as well as facility managers, in better management of building HVAC systems.
Highlights
In addition to data availability, understanding the association among faults and the suitability of data-driven algorithms to detect and diagnose them is essential for AFDD
The knowledge extracted from the wealth of reviewed machine learning techniques can aid in better comprehending the complexities that exist in HVAC systems at various levels
The first model developed in this study assists the asset managers/facility managers to better understand the associations among faults and anticipation of other fault types that can be expected when certain faults are identified in the HVAC system by the literature for the database considered
Summary
HVAC/R systems regulate the temperature, humidity, quality, and air movement in buildings, making them critical for occupant comfort, health, and productivity. In Canadian commercial stores, HVAC and lighting combined contribute to 90% of energy consumption [1]. In 2011, heating systems, furnaces (57%), followed by electric baseboards (27%) and boilers (5%), were the primary type of heating system used by Canadian households [2]. This energy consumption indicates the dependency of Canadian households and commercial buildings on the HVAC system, and emphasizes the importance of timely and accurate identification of its faults
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