Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network

LIU Xuting; LI Yiguo; SUN Shuanzhu; LIU Xichui; SHEN Jiong

doi:10.11949/j.issn.0438-1157.20180704

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Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network

更新时间：2025-12-10

- Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network
- CIESC Journal Issue 12, Pages: 5155-5163(2018)
- 作者机构：
  
  1. 东南大学能源与环境学院,江苏,南京,210096
  2. 东南大学能源热转换及其过程测控教育部重点实验室,江苏,南京,210096
  3. 江苏方天电力技术有限公司,江苏,南京,211102
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20180704
  CLC： P277
- Online First：05 December 2018，
  
  Published：05 December 2018
- 稿件说明：
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LIU Xuting, LI Yiguo, SUN Shuanzhu, et al. Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network[J]. CIESC Journal, 2018, (12): 5155-5163.
DOI：

LIU Xuting, LI Yiguo, SUN Shuanzhu, et al. Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network[J]. CIESC Journal, 2018, (12): 5155-5163. DOI： 10.11949/j.issn.0438-1157.20180704.

摘要

针对于冷水机组提出一种基于稀疏局部嵌入深度卷积网络（sparsely local embedding network，SLENet）的故障诊断方法。采用稀疏局部嵌入方法代替卷积核，对输入数据进行特征选择，避免了复杂的训练和调参过程。另外采用空间金字塔最大池化作为网络的输出层，减少了网络的输出维数和分类器的计算量。针对美国采暖、制冷与空调工程师学会提供的冷水机组的典型故障数据进行分类，结果表明，该方法相比深度卷积网络（CNN）和支持向量机（SVM）方法具有更高的故障诊断精度。

Abstract

A fault diagnosis method for chillers based on a sparsely local embedding deep convolutional neural network (sparsely local embedding network

SLENet) is proposed. The sparsely local embedding filters are adopted for feature selection in the first two layers of SLENet

as a result complicated training and adjusting process are avoided. In addition

the spatial pyramid max pooling layer is employed to construct the output layer of SLENet to reduce the output dimensions of the proposed network

and then alleviate the computationally burden of classification. The experimental data from ASHRAE RP-1043 are used to validate the fault diagnosis method. The results demonstrate that the method achieves high diagnostic accuracy compared to convolutional neural network (CNN) and support vector machine (SVM) methods.

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