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Chiller fault detection and diagnosis by knowledge transfer based on adaptive imbalanced processing

机译：基于自适应不平衡处理的知识转移，冷却器故障检测与诊断

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摘要

The existing fault detection and diagnosis (FDD) model of chillers requires considerable normal and fault data. The acquisition of these data is time-consuming and expensive, and the model is only suitable for special units, which makes it difficult to popularize FDD technology in the operation and management of chillers. At present, a 120-ton chiller has only a small amount of normal and fault data when compared with the abundant data of a 200-ton model of the same series. This study investigates the FDD model of a 120-ton chiller and considers similar characteristics of the refrigeration cycle of the same series of chillers. A training set can be created using the 200-ton prior-knowledge data and the 120-ton data. However, this training set is imbalanced, and the common imbalanced processing synthetic minority oversampling technique (SMOTE) synthesis mechanism has an overlap problem. This study adopts two adaptive imbalance processing technologies called the adaptive synthetic sampling approach (ADASYN) and borderline SMOTE (BSM) that can solve the imbalance problem and SMOTE oversampling overlap problem during knowledge transfer. A support vector machine FDD model with 100% to 400% oversampling ratios is established. The best model is ADASYN with less than 100% oversampling ratio, with a diagnostic accuracy rate of 94.33%.
机译：冷却器的现有故障检测和诊断（FDD）模型需要相当大的正常和故障数据。这些数据的获取是耗时和昂贵的，而该模型仅适用于特殊单位，这使得难以在冷却器的操作和管理中推广FDD技术。目前，与同一系列200吨模型的丰富数据相比，120吨冷却器仅具有少量的正常和故障数据。本研究研究了120吨冷却器的FDD模型，并考虑了同一系列冷却器的制冷循环的类似特性。可以使用200吨的先前知识数据和120吨数据来创建培训集。然而，这种训练集是不平衡的，并且常见的不平衡处理合成少数群体过采样技术（Smote）合成机制具有重叠问题。本研究采用了两个称为自适应合成采样方法（Adasyn）和边界扫描（BSM）的自适应不平衡处理技术，可以解决不平衡问题，并在知识转移期间阐明过采样的重叠问题。建立了100％至400％过采样比率的支持向量机FDD模型。最好的模型是adasyn，超采样比例小于100％，诊断准确率为94.33％。

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  • 作者

    Fan Yuqiang; Cui Xiaoyu; Han Hua; Lu Hailong;

  • 作者单位

    Univ Shanghai Sci &

    Technol Sch Energy &

    Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China;

    Univ Shanghai Sci &

    Technol Sch Energy &

    Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China;

    Univ Shanghai Sci &

    Technol Sch Energy &

    Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China;

    Univ Shanghai Sci &

    Technol Sch Energy &

    Power Engn 516 Jungong Rd Shanghai 200093 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑基础科学;
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