Research of fault diagnosis method of hydraulic pipeline cracks based on HHT

Computer Engineering and Applications ›› 2016, Vol. 52 ›› Issue (20): 221-226.

Previous Articles Next Articles

Research of fault diagnosis method of hydraulic pipeline cracks based on HHT

LI Zhezhu1，2, GAO Peixin3, TONG Kun4, ZHAO Dazhe1，2, LIU Jiren1，2

1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.Neusoft Group, Shenyang 110179, China
3.College of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
4.Zhongjie Machine Tools Co., Ltd, Shenyang 110142, China

Online:2016-10-15 Published:2016-10-14

基于HHT的液压管路裂纹故障诊断方法研究

李哲洙1，2，高培鑫3，佟琨4，赵大哲1，2，刘积仁1，2

1.东北大学信息科学与工程学院，沈阳 110819
2.东软集团，沈阳 110179
3.大连理工大学机械工程学院，辽宁大连 116024
4.中捷机床有限公司，沈阳 110142

Abstract

Abstract: In this paper, the HHT method is applied to detect the crack faults of hydraulic pipeline. The crack faults diagnosis of hydraulic pipeline method based on HHT is proposed, and an experimental application to the normal hydraulic pipeline and a crack hydraulic pipeline demonstrates the effectiveness of the detection technique. First, EMD（Empirical Mode Decomposition） vibration signal decomposition is done. The EMD and HHT method is introduced to analyze the vibration signals of the aeroengine hydraulic pipeline cracks. The results of the vibration signal of an engine hydraulic pipeline cracks show that the method can overcome the defects that the time domain and frequency domain cannot be obtained at the same time by Fourier spectrum. And at the same time, the marginal spectrum can more objectively reflect the frequency and amplitude distribution of the cracks hydraulic pipeline. In addition the marginal spectrogram shows that no cracks in hydraulic pipeline and cracks hydraulic pipeline vibration signal frequency energy focuse on 25 Hz, respectively, the whole system stiffness of cracked is greater than that of no cracks. Therefore, the vibration increase of cracks pipeline can be expressed by HHT method obviously.

Key words: hydraulic pipeline cracks, Hilbert-Huang transform, fault diagnosis, empirical mode decomposition

摘要： 将HHT方法应用于液压管路裂纹的故障诊断，提出基于HHT的液压管路裂纹故障诊断方法，并以正常液压管路和有裂纹液压管路为例进行实验验证。首先进行EMD（经验模态分解法）振动信号分解。将EMD和HHT方法引入航空发动机液压管路裂纹的振动信号分析，某发动机液压管路的裂纹振动信号的分析结果表明，该方法能够克服傅里叶谱无法同时获得时域和频域信息的缺陷。同时边际谱能够比较真实客观地反映有裂纹液压管路的频率和幅值分布情况。此外由边际频谱图中可知，无裂纹液压管路、有裂纹液压管路振动信号的频率能量分别集中于25 Hz，有裂纹的整体系统刚度大于无裂纹的。据此，有裂纹的管路，其振动加大的现象得以由HHT方法明显呈现。

关键词: 液压管路裂纹, 希尔伯特黄转换（HHT）, 故障诊断, 经验模态分解法（EMD）

LI Zhezhu1，2, GAO Peixin3, TONG Kun4, ZHAO Dazhe1，2, LIU Jiren1，2. Research of fault diagnosis method of hydraulic pipeline cracks based on HHT[J]. Computer Engineering and Applications, 2016, 52(20): 221-226.

李哲洙1，2，高培鑫3，佟琨4，赵大哲1，2，刘积仁1，2. 基于HHT的液压管路裂纹故障诊断方法研究[J]. 计算机工程与应用, 2016, 52(20): 221-226.

[1]	LIN Ronglai, TANG Bingying, CHEN Ming. Data Augmentation Algorithm for Bearings Faults Diagnosis [J]. Computer Engineering and Applications, 2021, 57(7): 269-278.
[2]	HAN Weiyu, CHENG Longsheng. Research on Roling Bearing Failure Mode Classification Based on MTS and SVM [J]. Computer Engineering and Applications, 2021, 57(6): 239-246.
[3]	YAO Honggang, MU Nianguo. Prediction of Financial Time Series by EMD-LSTM Model [J]. Computer Engineering and Applications, 2021, 57(5): 239-244.
[4]	CHEN Mingyue, LIU Sanyang. Application of Adaptive Manifold Learning in Fault Diagnosis [J]. Computer Engineering and Applications, 2021, 57(3): 247-252.
[5]	LI Ya, HOU Yandong, LIU Chang. Adaptive Optimal Fault-Tolerant Control Based on Fault Degree [J]. Computer Engineering and Applications, 2021, 57(23): 295-302.
[6]	YI Lingzhi, WANG Shitong, YI Fang, DENG Dong, YI Zhimin, JIANG Peng. Wind Farm Ultra-Short-Term Wind Speed Prediction Based on EEMDSE-ILSTM [J]. Computer Engineering and Applications, 2021, 57(22): 288-294.
[7]	QIAN Wenbo, XIONG Jianbin, CEN Jian, WANG Qi, YU Dezheng, WU Runjie. Review of Fault Diagnosis of Building Electrical System [J]. Computer Engineering and Applications, 2021, 57(16): 27-39.
[8]	LI Guang’an, CAO Yan, YUE Xiaoxin. Infrared and Visible Image Fusion Based on BEEMD Decomposition [J]. Computer Engineering and Applications, 2021, 57(14): 237-244.
[9]	TANG Dengping, CAI Wenjia, ZOU Li, HU Xiang, DING Li, WANG Xue. Application of Improved Convolution Neural Network in Fault Diagnosis of Transformer [J]. Computer Engineering and Applications, 2021, 57(11): 239-247.
[10]	ZHONG Lusheng, LIU Dongdong. Research on Bearing Fault Diagnosis of Multi-level Neural Network [J]. Computer Engineering and Applications, 2020, 56(7): 193-199.
[11]	DU Xiaolei, CHEN Zhigang, XU Xu, ZHANG Nan. Method of Improved Deep Wavelet Auto-Encoder in Bearing Fault Diagnosis [J]. Computer Engineering and Applications, 2020, 56(5): 263-269.
[12]	YU Ping, CAO Jie. Deep Learning Approach and Its Application in Fault Diagnosis and Prognosis [J]. Computer Engineering and Applications, 2020, 56(3): 1-18.
[13]	MAO Biao, YANG Song, LI Yingshun. Improved Dynamic Causality Diagram and Fuzzy Inference Fusion Fault Diagnosis Method [J]. Computer Engineering and Applications, 2020, 56(3): 259-265.
[14]	TANG Wei, ZHENG Yuan, PAN Hong, XU Jingjun. Roller Bearing Fault Diagnosis Based on Stacked Auto-encoder with Dynamic Learning Rate [J]. Computer Engineering and Applications, 2020, 56(20): 264-269.
[15]	WANG Hui, LI Yingshun. Dynamic Fault Diagnosis Algorithm for Improved Evidence Update Rules [J]. Computer Engineering and Applications, 2020, 56(2): 261-265.

Research of fault diagnosis method of hydraulic pipeline cracks based on HHT

基于HHT的液压管路裂纹故障诊断方法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics