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

doi:10.11949/j.issn.0438-1157.20180704

Abstract

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.

Key words: algorithm, neural networks, safety, fault diagnosis, sparsely local embedding, deep convolutional neural network, spatial pyramid max pooling

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。

关键词: 算法, 神经网络, 安全, 故障诊断, 稀疏局部嵌入, 深度卷积网络, 空间金字塔最大池化

CLC Number:

TP277

LIU Xuting, LI Yiguo, SUN Shuanzhu, LIU Xichui, SHEN Jiong. Fault diagnosis of chillers using sparsely local embedding deep convolutional neural network[J]. CIESC Journal, 2018, 69(12): 5155-5163.

刘旭婷, 李益国, 孙栓柱, 刘西陲, 沈炯. 基于稀疏局部嵌入深度卷积网络的冷水机组故障诊断方法[J]. 化工学报, 2018, 69(12): 5155-5163.

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