| 基于声发射和长短时记忆神经网络的端对端燃气高压调压器故障诊断 |
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| 引用本文： | 王玉玲,陈涛涛,李红浪. 基于声发射和长短时记忆神经网络的端对端燃气高压调压器故障诊断[J]. 声学技术, 2021, 40(6): 883-889 |
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| 作者姓名： | 王玉玲 陈涛涛 李红浪 |
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| 作者单位： | 中国科学院大学, 北京 100049;中国科学院声学研究所, 北京 100190;北京燃气集团有限责任公司, 北京 100011;中国科学院大学, 北京 100049;中国科学院纳米科学卓越创新中心, 国家纳米科学中心, 北京 100190 |
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| 基金项目： | 北京市燃气集团高压管网分公司支持项目(19JK011)。 |
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| 摘 要： | 在燃气输配系统中燃气调压器处于关键位置,及时有效地诊断其故障类型具有重要意义.文章提出了一种基于声发射信号和长短时记忆神经网络的端到端故障诊断方法,直接利用时域声发射信号对调压器的运行状态进行诊断.首先,设计一个二阶巴特沃斯高通滤波器对采集的声发射信号进行预处理;其次,利用长短时记忆网络(Long Short Term...
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| 关 键 词： | 故障诊断 声发射信号 长短时记忆网络 端到端方法 |
| 收稿时间： | 2020-07-01 |
| 修稿时间： | 2020-07-19 |
Acoustic emission and LSTM based end-to-end fault diagnosis of gas high-pressure regulator |
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| WANG Yuling,CHEN Taotao,LI Honglang. Acoustic emission and LSTM based end-to-end fault diagnosis of gas high-pressure regulator[J]. Technical Acoustics, 2021, 40(6): 883-889 |
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| Authors: | WANG Yuling CHEN Taotao LI Honglang |
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| Affiliation: | University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Acoustics, Chinese Academy of Science, Beijing 100190, China;Beijing Gas Group Co., Ltd., Beijing 100011, China; University of Chinese Academy of Sciences, Beijing 100049, China;CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China |
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| Abstract: | In the gas transmission and distribution system, the gas pressure regulator is in a key position for the timely and effective diagnosis of its fault types. In this paper, a long short term memory network (LSTM) based end-to-end fault diagnosis method is proposed, in which the acoustic emission signals is used to diagnose the operation condition of the gas pressure regulator. First, a second-order Butterworth high-pass filter is designed to preprocess the acquired acoustic emission signals. Then, by using the memory characteristics of long short term memory networks, an e2e-LSTM fault diagnosis model is established. The experimental results show that, with the input acoustic emission signals collected from the high-pressure regulator station, the model can diagnose five kinds of high-pressure regulator faults at a time in the end-to-end mode. |
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| Keywords: | fault diagnosis acoustic emission signal long short term memory networks (LSTM) end-to-end method |
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