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首页> 外文期刊>Energy >Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system
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Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system

机译：基于数据驱动的建筑能源系统故障诊断的知识发现：以建筑可变制冷剂流量系统为例

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摘要

The data-driven-based methods, which rely on history data, are the most common methods used in the fault diagnostics of building energy system because of their simplicity. However, a major problem with the application of data-driven methods is its interpretability due to the complicated algorithm theory and structure. This paper therefore proposes a methodology which is able to conduct both fault diagnosis and diagnostic knowledge discovery for building energy systems. A case study is implemented in an experimental variable refrigerant flow (VRF) system. The clustering of variable around latent variables (CLV) method is used for variable selection. Then, a classification-based-on-associations (CBA) classifier is set up for fault diagnosis based on the mined association rules. It achieves an overall diagnosis accuracy of 95.33%. In addition, the class association rules (CARs) of the classifier are visualized by grouped matrix-based method and graph-based method, respectively. Further, the CARs with high confidences and supports are interpreted by domain knowledge in the individual fault level. Results show that the diagnostic outcomes comply well with the expert knowledge. The underlying system operational characteristics at faulty conditions could be mined and understood. Moreover, the diagnostic outcomes provide a reasonable and reliable reference for further FDD researches. (C) 2019 Elsevier Ltd. All rights reserved.
机译：依靠历史数据的基于数据驱动的方法由于其简单性而成为建筑能源系统故障诊断中最常用的方法。但是，由于复杂的算法理论和结构，数据驱动方法的应用存在的主要问题是其可解释性。因此，本文提出了一种能够对建筑能源系统进行故障诊断和诊断知识发现的方法。在实验可变制冷剂流量（VRF）系统中进行了案例研究。变量围绕潜在变量的聚类（CLV）方法用于变量选择。然后，基于挖掘的关联规则，建立了基于关联的分类（CBA）分类器，用于故障诊断。总体诊断准确率达到95.33％。此外，分别通过基于矩阵的分组方法和基于图的方法可视化分类器的类关联规则（CAR）。此外，具有高置信度和高支持度的CAR由各个故障级别的领域知识来解释。结果表明，诊断结果符合专家知识。可以挖掘和了解故障条件下的基本系统运行特性。此外，诊断结果为进一步的FDD研究提供了合理而可靠的参考。 （C）2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy》 |2019年第1期|873-885|共13页
  • 作者

    Liu Jiangyan; Li Guannan; Liu Bin; Li Kuining; Chen Huanxin;

  • 作者单位

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Hubei, Peoples R China|Chongqing Univ, Sch Energy & Power Engn, Chongqing 400044, Peoples R China;

    Wuhan Univ Sci & Technol, Sch Urban Construct, Wuhan, Hubei, Peoples R China;

    Chongqing Univ, Sch Energy & Power Engn, Chongqing 400044, Peoples R China;

    Chongqing Univ, Sch Energy & Power Engn, Chongqing 400044, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Wuhan 430074, Hubei, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fault diagnosis; Knowledge discovery; Data-driven; Building energy system;

    机译：故障诊断;知识发现;数据驱动;建筑能源系统;

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