Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission

您当前的位置：

首页 >

文章列表页 >

Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission

更新时间：2025-07-09

- Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission
- Vol. 46, Issue 9, Pages: 26-30(2012)
- 作者机构：
  
  1. 西安交通大学能源与动力工程学院,西安,710049
  2. 中国科学院理化技术研究所,北京,100190
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH457
- Online First：10 September 2012，
  
  Published：2012
- 稿件说明：
移动端阅览
Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission[J]. 2012, 46(9): 26-30.
DOI：

Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission[J]. 2012, 46(9): 26-30. DOI：

摘要

利用声发射信号对往复空压机气阀进行了故障诊断

并以某往复空压机的排气阀为例

结合有效电压和能量对气阀漏气、阀片裂纹、阀片断裂及阀片变形等故障进行了测量

从而获得了故障特征信号.研究结果表明:随转角变化的声发射信号包络波形能体现排气阀启、闭的特征位置

气阀漏气、阀片变形及断裂等会导致气阀启、闭特征位置发生变化; 根据启、闭特征位置偏离度及相应的有效电压和能量

可以判断气阀故障的严重程度; 声发射包络波形平均幅值及相应的有效电压和能量

可以体现气阀漏气、阀片变形及断裂等故障.通过试验结果还发现

阀片裂纹不能通过声发射信号来识别.

Abstract

The faults of self-acting valves in a reciprocating air compressor can be diagnosed using the acoustic emission. Four typical valve faults including the crack

rupture and deformation in the valve discs and the leakage through the flow passage were investigated. The acoustic emission of the failed discharge valves was collected from an air reciprocating compressor. The fault features were extracted by comparing the acoustic emission signals from the failed valves with those from the normal valves. The results show that the characteristic locations of the opening and closing of the discharge valve could easily be determined by the envelope waveform of the acoustic emission signals

and the changes in these locations were identified as the valve faults including the rupture and deformation in the valve discs and leakage. The extent to which the valve failed could be estimated by the deviation of the opening/closing locations from the standard ones

the RMS

and the energy. The rupture and deformation in the valve discs and leakage could also lead to the increase in the amplitude of the acoustic emission wave

the value of RMS

and the energy. However

the crack in valve discs could not be identified by acoustic emission signals effectively.

关键词

Keywords

references

林京,刘红星,屈梁生.功率谱谱距及其模糊测度在气阀故障诊断中的应用[J].化工机械,1997(3):168-170.

LIN Jing, LIU Hongxing, QU Liangsheng. Fault diagnosis of valve using power spectrum distance and fuzzy measure [J]. Chemical Machinery, 1997(3): 168-170.

ELHAJ M, ALMRABET M, RGEAI M, et al. A combined practical approach to condition monitoring of reciprocating compressor using IAS and dynamic pressure [J]. World Academy of Science, Engineering and Technology, 2010(39): 186-192.

MANEPATIL S S, TIWARI A. Fault diagnosis of reciprocating compressor using pressure pulsations [C]∥International Compressor Engineering Conference. Purdue, USA: University of Purdue, 2006: 17-20.

孙嗣莹,何云涛,任廷荣.往复式压缩机填料密封的机械故障诊断方法 [J].西安交通大学学报, 1996, 30(8): 51-56.

SUN Siying, HE Yuntao, REN Tingrong. Machinery fault diagnosis of packing seal in reciprocating compressor [J]. Journal of Xi'an Jiaotong University, 1996, 30(8): 51-56.

王晓冬,何正嘉,訾艳阳. 滚动轴承故障诊断的多小波谱峭度方法 [J].西安交通大学学报, 2010, 44(3): 77-81.

WANG Xiaodong, HE Zhengjia, ZI Yanyang. Spectral kurtosis of multiwavelet for fault diagnosis of rolling bearing [J]. Journal of Xi'an Jiaotong University, 2010, 44(3): 77-81.

SONGPON K, PORNCHAI N. Condition monitoring of valve clearance fault on a small four strokes petrol engine using vibration signals [J]. Songklanakarin Journal of Science and Technology, 2010, 32(6): 619-625.

AHMED M, GU F, BALL A D. Fault classification using an artificial neural network based on vibrations from a reciprocating compressor [C]∥Computing and Engineering Researchers' Conference. Huddersfield, UK: University of Huddersfield, 2010: 92-97.

ELAMIN F, FAN Y, GU F, et al. Detection of diesel engine valve clearance by acoustic emission [C]∥Computing and Engineering Researchers' Conference. Huddersfield, UK: University of Huddersfield, 2009: 7-13.

王潜龙,冯全科,屈展,等.基于声发射与小波包理论的压力管道泄漏检测 [J].西安交通大学学报, 2003, 37(5): 515-518.

WANG Qianlong, FENG Quanke, QU Zhan, et al. Pressure pipe leakage detection based on the acoustic emission and wavelet packet theory [J]. Journal of Xi'an Jiaotong University, 2003, 37(5): 515-518.

杨剑锋,李良,张斌.活塞杆疲劳裂纹声发射监测的实验研究 [J].无损探伤, 2007, 31(4): 21-23.

YANG Jianfeng, LI Liang, ZHANG Bin. Experimental research on the piston rod fracture detection using acoustic emission [J]. Nondestructive Inspection, 2007, 31(4): 21-23.

马波,高金吉,江志农.往复压缩机活塞杆断裂早期预警技术的研究 [J].机械强度, 2008, 30(3): 445-449.

MA Bo, GAO Jinji, JIANG Zhinong. Research on the piston rod fracture early warning technique of reciprocating compressor [J]. Journal of Mechanical Strength, 2008, 30(3): 445-449.

蒋旭鑫,曹军义,李锐,等.基于声发射和关联维数的空气压缩机故障诊断技术研究 [J]. 机械强度, 2011, 33(6): 803-808.

JIANG Xuxin, CAO Junyi, LI Rui, et al. Fault diagnosis of air compressor based on AE signals and correlation dimension [J]. Journal of Mechanical Strength, 2011, 33(6): 803-808.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis

Dual-Wavelet Dictionary Based Feature Extraction Method for Compound Faults in Gearboxes

Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction

Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

Application of Interpretable Convolutional Neural Network Enabled by Bi-Damped Wavelets in Bearing Fault Diagnosis

Related Author

LIN Jinshan

LI Yuan

FANG Qixuan

LIU Zhihao

LI Chunpeng

ZHANG Guanjun

WANG Xiangdong

LIU Qiang

Related Institution

西安交通大学电气工程学院，710049，西安School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

College of Hydraulic & Environmental Engineering, China Three Gorges University

China Yangtze Power Co., Ltd.

College of Mechanical and Power Engineering, China Three Gorges University

Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University

⁰