Fault Diagnosis of Chillers Based on Neural Network and Wavelet Denoising

Shi Shubiao; Chen Huanxin; Li Guannan; Hu Yunpeng; Li Haorong; Hu Wenju

doi:10.3969/j.issn.0253-4339.2016.01.012

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Fault Diagnosis of Chillers Based on Neural Network and Wavelet Denoising

更新时间：2024-07-18

- Fault Diagnosis of Chillers Based on Neural Network and Wavelet Denoising
- Journal of Refrigeration Vol. 37, Issue 1, (2016)
- 作者机构：
  
  1. 华中科技大学制冷与低温实验室
  2. University of Nebraska,Lincoln
  3. 北京建筑大学供热供燃气通风及空调工程北京市重点实验室
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0253-4339.2016.01.012
  CLC：
- Published：2016
- 稿件说明：
移动端阅览
Shi Shubiao, Chen Huanxin, Li Guannan, et al. Fault Diagnosis of Chillers Based on Neural Network and Wavelet Denoising[J]. Journal of refrigeration, 2016, 37(1).
DOI：

Shi Shubiao, Chen Huanxin, Li Guannan, et al. Fault Diagnosis of Chillers Based on Neural Network and Wavelet Denoising[J]. Journal of refrigeration, 2016, 37(1). DOI： 10.3969/j.issn.0253-4339.2016.01.012.

摘要

对基于神经网络方法的冷水机组故障监测效率取决于训练数据和被测数据的质量的问题进行了研究。采用小波变换的方法剔除测量数据中的噪声，提高数据质量，从而提高冷水机组故障诊断效率。结果表明：采用小波变换使得各个水平故障的检测效率均得到提高，尤其水平一的故障检测效率提高明显。故障水平一检测率的提高能够及时的辨别冷水机组的故障，从而采用措施防止故障进一步恶化，对降低能源消耗、提高系统的可靠性以及保证室内舒适性具有重要的意义。通过利用ASHRAE Project提供的数据对故障诊断与检测(fault detection and diagnosis)策略进行验证，检测率明显提高。

Abstract

Chiller fault detection based on neural network is a data-based analysis method. The fault detection efficiency relies on the quality of the training data and the mesasured data.The wavelet transfer method which can remove the measurement nosise is used to improve the detection efficiencies of chiller.The results show that wavelet transfer make the detection efficiencies of fault level improved

especially the first level. The increase of the first level detection rate will be able to timely identify the chiller fault

and take the measures to prevent further deterioration of chiller fault

which is of important significance to reduce energy consumption and improve the reliability of the air-conditioning system and ensure the indoor thermal comfort. The FDD (fault detection and diagnosis）strategy is validated through using ASHRAE Project data

which shows that the detection rate is improved obviously.

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