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首页> 外文期刊>Energy and Buildings >Ensemble learning with member optimization for fault diagnosis of a building energy system
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Ensemble learning with member optimization for fault diagnosis of a building energy system

机译：与成员优化的集合学习，用于建筑能量系统的故障诊断

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摘要

For better service and energy savings, improved fault detection and diagnosis (FDD) of building energy systems is of great importance. To achieve this aim, ensemble learning is investigated and introduced in this study. Three types of methods like K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) are carefully selected, optimized, and integrated into an ensemble diagnostic model (EDM) by the majority voting method. Experimental data for seven typical gradual faults in a centrifugal building chiller are used for model validation and evaluation. The results show that the diagnostic accuracy of the EDM (99.88%) is higher than that of the individual methods, with significant improvements for normal operation and refrigerant leakage, and no false alarms reported. Models based on ensemble learning, EDM and RF (homogenous ensemble), exhibit better performance for global faults, which are difficult to diagnose. In addition, five different feature sets are selected from the literature for further tests. It is found that the diagnostic performance depends not only on the principle of diagnosis, but also on the fault category and the characteristics of the feature set such as the indicative degree to corresponding faults, number of features, correlation degree between features, and information redundancy, and feature selection is proved to be more important than algorithm selection in fault diagnosis practice. Ensemble learning is proved to be a promising candidate for the fault diagnosis of building energy systems, except for the Zhou-8 feature set (eight temperature features), for which KNN is a better choice. (C) 2020 Elsevier B.V. All rights reserved.
机译：为更好的服务和节能，建筑能源系统的改善故障检测和诊断（FDD）具有重要意义。为实现这一目标，在本研究中调查并介绍了集合学习。三种类型的方法如K-Collect邻（KNN），支持向量机（SVM）和随机林（RF）被仔细选择，优化，并通过大多数投票方法集成到集合诊断模型（EDM）中。离心式建筑冷却器中七种典型渐变故障的实验数据用于模型验证和评估。结果表明，EDM的诊断精度（99.88％）高于各个方法的诊断精度，具有正常操作和制冷剂泄漏的显着改进，并且没有报告错误警报。基于集合学习的模型，EDM和RF（均匀合奏），对全球故障表现出更好的性能，这很难诊断。此外，从文献中选择五种不同的特征集以进行进一步的测试。结果发现，诊断性能不仅取决于诊断原则，还取决于故障类别和特征集的特征，例如指示程度对应的故障，特征之间的特征数，相关程度以及信息冗余并且，特征选择被证明比故障诊断实践中的算法选择更重要。除了周-8特征集（八个温度特征）外，被证明是建筑能源系统的故障诊断的有前途的候选者，除了knn是更好的选择。 （c）2020 Elsevier B.v.保留所有权利。

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  • 来源
    《Energy and Buildings》 |2020年第11期|110351.1-110351.14|共14页
  • 作者

    Han Hua; Zhang Zhan; Cui Xiaoyu; Meng Qinghong;

  • 作者单位

    Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai Key Lab Multiphase Flow & Heat Transfer Shanghai 200093 Peoples R China;

    Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai Key Lab Multiphase Flow & Heat Transfer Shanghai 200093 Peoples R China;

    Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai Key Lab Multiphase Flow & Heat Transfer Shanghai 200093 Peoples R China;

    Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai Key Lab Multiphase Flow & Heat Transfer Shanghai 200093 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Refrigeration system; Fault diagnosis; Ensemble learning; Majority voting; Features;

    机译：制冷系统;故障诊断;集合学习;大多数投票;特征;

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