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Simulated Annealing Wrapped Generic Ensemble Fault Diagnostic Strategy for VRF System

机译：模拟退火包装的VRF系统包装泛型集合故障诊断策略

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摘要

Variable refrigerant flow (VRF) systems have gained much attention and been widely used in commercial and residential buildings benefitting from their competitive advantages. However, after long-term oper-ation in a complex environment, various faults may occur in the VRF systems, resulting in failure to meet users comfort requirements and even unnecessary increase in energy consumption. This paper proposes a simulated annealing wrapped generic ensemble fault diagnosis strategy for typical faults of VRF systems, such as refrigerant charge amount (RCA) faults, valve faults, and compressor liquid return (LF) faults. The simulated annealing algorithm based on random forest (SA-RF) is first utilized to perform feature selec-tion process on the three kinds of fault datasets to select the optimal variables that can well characterize the fault states, which can improve the modeling efficiency while reducing the data dimensionality. Then five component learners and the proposed ensemble model based on them are established adopting the optimal variables as input variables. Through visualizing the error evolution and margin of the boosting models built in the first stage of the integration process, it was found that the boosting models can effec-tively avoid overfitting and most samples are correctly classified with high confidence. By comparing with the five component learners, it is concluded that the boosting strategy in the first stage can improve the diagnostic performance of the models, and the weighted voting integration strategy in the second stage can further improve the diagnostic performance of the model. The final ensemble model can effec-tively compensate for the deficiencies of each component learners and its diagnostic accuracy for the three fault data sets is as high as 95.37%, 99.36% and 98.3%, respectively, indicating that the model can be applied to diagnose the three types of faults in VRF system at the same time, showing a high versatility. (C) 2020 Elsevier B.V. All rights reserved.
机译：可变制冷剂流量（VRF）系统已获得大量关注，并广泛用于商业和住宅建筑，受益于其竞争优势。然而，在复杂环境中长期运算过程中，在VRF系统中可能发生各种故障，导致未能满足用户的舒适要求，甚至不必要的能耗增加。本文提出了一种模拟退火包装包装的通用集合故障诊断策略，用于VRF系统的典型故障，如制冷剂电荷量（RCA）故障，阀门故障和压缩机液体返回（LF）故障。首先利用基于随机林（SA-RF）的模拟退火算法在三种故障数据集上执行特征选择性过程，以选择可以很好地表征故障状态的最佳变量，这可以提高建模效率减少数据维度。然后，基于它们的五个组件学习者和所提出的集合模型，建立了作为输入变量的最佳变量。通过可视化集成过程的第一阶段内置的升压模型的错误演化和边缘，发现增强模型可以有效避免过度装备，大多数样本都以高信心正确归类。通过与五个组件学习者进行比较，得出结论，第一阶段的升压策略可以提高模型的诊断性能，第二阶段的加权投票集成策略可以进一步提高模型的诊断性能。最终的集合模型可以有效地弥补每个组件学习者的缺陷，并且其三个故障数据集的诊断精度分别高达95.37％，99.36％和98.3％，表明该模型可以应用于诊断VRF系统中的三种故障同时显示出高通用性。 （c）2020 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《Energy and Buildings》 |2020年第10期|110281.1-110281.19|共19页
  • 作者

    Li Zhengfei; Wei Wentian; Hu Kuan; Chen Huanxin; Wang Yuzhou; Liu Qian; Liu Shuai;

  • 作者单位

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China;

    Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 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

    VRF system; Simulated annealing algorithm; Ensemble learning; Refrigerant; Valves; Liquid floodback; Fault diagnosis;

    机译：VRF系统;模拟退火算法;集合学习;制冷剂;阀门;液体洪水;故障诊断;

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