Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Li, Guannan; Hu, Yunpeng; Liu, Jiangyan; Fang, Xi; Kang, Jaewon

doi:10.1109/access.2020.3040980

Cited by 35 publications

(10 citation statements)

References 299 publications

(275 reference statements)

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“…This file is available in a GitHub repository [36] and is open for additional contributions. Zhao et al [21], Katipamula et al [11], Woohyun and Katipamula [14], Gourabpasi and Nik-Bakht [20], Li et al [22], Shi et al [26], and Ahmad et al [12] were not included due to similar definitions and not the focus of these reviews. Zhang et al's [38] data-based methods and model-based methods were chosen as the base for further division of the articles, since (1) this article mainly concerned a bibliographic review of FTC, but also discussed the FDD process; and (2) these definitions were simplistic.…”

Section: Methods Categorizations For Fddmentioning

confidence: 99%

“…As this review was focused on creating a common glossary and understanding of FDD, the specific workings of the different algorithms are not explained. To read more about the details of each algorithm, it is suggested to read either the individual articles associated with each algorithm, or see the previously mentioned literature reviews, especially for building systems [12,14,22,[78][79][80] for a three-part review on fault detection and diagnosis explicitly targeting the characteristics of each algorithm.…”

Section: Algorithm Distributionmentioning

confidence: 99%

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Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

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Section: Methods Categorizations For Fddmentioning

confidence: 99%

Section: Algorithm Distributionmentioning

confidence: 99%

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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“…Machine learning emerged as a development of KBSs. Based on the historical maintenance data and expert knowledge, machine-learning algorithms such as Bayesian network (BN) [14], random forest [15], and support vector machine (SVM) [16] were developed with feature engineering [17][18][19][20] to build models for fault diagnosis. The growing number of parameters and data, quite often from heterogeneous sources, has led to the big data challenge [21].…”

Section: Introductionmentioning

confidence: 99%

Knowledge transfer in fault diagnosis of rotary machines

Liu

Shen

Gao

et al. 2022

IET Collab Intel Manufact

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Data‐driven fault diagnosis has prevailed in machine condition monitoring in the past decades. However, traditional machine‐ and deep‐learning‐based fault diagnosis methods assumed that the source and target data share the same distribution and ignored knowledge transfer in dynamic working environments. In recent years, knowledge transfer approaches have been developed and have shown promising results in intelligent fault diagnosis and health management of rotary machines. This paper presents a comprehensive review of knowledge transfer approaches and their applications in fault diagnosis of rotary machines. A problem‐oriented taxonomy of knowledge transfer in fault diagnosis is proposed. The knowledge transfer paradigms, approaches, and applications are categorised and analysed. Future research challenges and directions are explored from data, modelling, and application perspectives.

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“…There has been a great deal of interest in reviewing the field of HVAC FDD methods. Recent reviews present trends in the use of FDD methods, explain the methodology of each commonly used algorithm, and develop new ways to categorize FDD methods and techniques [ 1 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Li et al [ 6 ] specifically reviewed FDD methods with a focus on feature engineering in FDD.…”

Section: Introductionmentioning

confidence: 99%

“…Recent reviews present trends in the use of FDD methods, explain the methodology of each commonly used algorithm, and develop new ways to categorize FDD methods and techniques [ 1 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Li et al [ 6 ] specifically reviewed FDD methods with a focus on feature engineering in FDD. Next, Hosseini Gourabpasi and Nik-Bakht [ 7 ] developed knowledge discovery models that inferred common researched faults and used features along with their connections to different parts of the HVAC system.…”

Section: Introductionmentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

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Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Cited by 35 publications

References 299 publications

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Knowledge transfer in fault diagnosis of rotary machines

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

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