Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary

He, Suowei; Wang, Zhiwei; Wang, Zhanwei; Gu, Xiaowei; Yan, Zengfeng

doi:10.1016/j.applthermaleng.2016.06.153

Cited by 67 publications

(16 citation statements)

References 13 publications

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“…Bayesian networks (BN) use graph theory and probability theory to perform data analysis and are preferred for their reasoning ability. In the reviewed literature, it was evident that BN is mostly used in system-level studies to detect and diagnose HVAC faults and support literature analysis results found for the type of HVAC used [43]. BN's distinct advantage is the ability to identify the causes and sort them from the most to the least probable, which can be used to prioritize inspection and maintenance [39].…”

Section: Data-driven Fdd Algorithms Based On Machine Learning Approachmentioning

confidence: 68%

Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC

Gourabpasi

Nik-Bakht

2021

CivilEng

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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.

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Section: Data-driven Fdd Algorithms Based On Machine Learning Approachmentioning

confidence: 68%

Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC

Gourabpasi

Nik-Bakht

2021

CivilEng

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“…The severity level of the fault can also be determined. In [19], He et al proposed a FD method for chillers using Bayesian network classifier with probabilistic boundary. After integrating the probabilistic boundary, the false alarm rate can be significantly reduced.…”

Section: Review Of the Existing Data-driven Fault Diagnosis Methods F...mentioning

confidence: 99%

A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data

Cheng

Zhang

et al. 2021

Applied Energy

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“…Table 4 summarizes the major supervised learning algorithms used in building HVAC system FDD and their applications. Linear Discriminant Analysis (LDA) [60] Linear Classifier [61], [62] Bayesian Network Classifier (BNC) [63], [64] Air Terminal unit SVM [70] Whole system ANN [71] Apart from supervised learning, clustering is also applied in FDD to preprocess the time series dataset for grouping diverse system behaviors.…”

Section: Machine Learning For Building Operation and Maintenancementioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

229

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary

Cited by 67 publications

References 13 publications

Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC

Knowledge Discovery by Analyzing the State of the Art of Data-Driven Fault Detection and Diagnostics of Building HVAC

A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data

State-of-the-art on research and applications of machine learning in the building life cycle

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