Abstract
Leakage is one of the most important reasons for failure of hydraulic systems. The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems. For early stage of leakage, the pressure of the hydraulic circuit does not change obviously and therefore cannot be monitored by pressure sensors. Meanwhile, the pressure of the hydraulic circuit changes frequently due to the influence of load and state of the switch, which further reduces the accuracy of leakage localization. In the work, a novel Bayesian networks (BNs)-based data-driven early leakage localization approach for multi-valve systems is proposed. Wavelet transform is used for signal noise reduction and BNs-based leak localization model is used to identify the location of leakage. A normalization model is developed to improve the robustness of the leakage localization model. A hydraulic system with eight valves is used to demonstrate the application of the proposed early micro-leakage detection and localization approach.
摘要
泄漏是引起液压系统失效最主要的原因, 泄漏的精确诊断与定位对保障液压系统的正常运行具有重要意义。在泄漏发生早期, 液压系统的压力信号没有明显变化, 压力传感器难以对其进行监测。同时, 系统的压力受到负载及开关状态改变而变化, 这将导致传感器信号变化剧烈而进一步加剧泄漏检测与定位难度。本文提出了一种数据驱动的液压系统早期微小泄漏检测与定位方法, 利用小波变化对信号进行降噪处理, 建立基于Bayesian 网络的泄漏检测与定位模型进行泄漏检测, 通过归一化模型将不同压力下声发射信号特征值转化为目标压力下声发射信号特征值, 以提高压力鲁棒性。该方法在实验室的液压系统上得到了验证。
Similar content being viewed by others
References
HU Jian-jun, CHEN Jin, QUAN Ling-xiao, KONG Xiang-dong. Flow measurement and parameter optimization of right-angled flow passage in hydraulic manifold block [J]. Journal of Central South University, 2019, 26(4): 852–864. DOI: https://doi.org/10.1007/s11771-019-4054-2.
MARGHANY M. Automatic mexico gulf oil spill detection from radarsat-2 SAR satellite data using genetic algorithm [J]. Acta Geophysica, 2016, 64(5): 1916–1941. DOI: https://doi.org/10.1515/acgeo-2016-0047.
SHANBHAG V V, MEYER T J, CASPERS L W, SCHLANBUSCH R. Condition monitoring of hydraulic cylinder seals using acoustic emissions [J]. The International Journal of Advanced Manufacturing Technology, 2020, 109(5, 6): 1727–1739. DOI: https://doi.org/10.1007/s00170-020-05738-4.
WAN Wu-yi, ZHANG Bo-ran. The intermittent leakage phenomenon of incipient cracks under transient conditions in pipeline systems [J]. International Journal of Pressure Vessels and Piping, 2020, 186: 1–8. DOI: https://doi.org/10.1016/j.ijpvp.2020.104138.
ZHOU Zi-long, CHENG Rui-shan, CHEN Lian-jun, ZHOU Jing, CAI Xin. An improved joint method for onset picking of acoustic emission signals with noise [J]. Journal of Central South University, 2019, 26(10): 2878–2890. DOI: https://doi.org/10.1007/s11771-019-4221-5.
ZHANG Hao-ran, LIANG Yong-tu, ZHANG Wan, XU Ning, GUO Zhi-ling, WU Guang-ming. Improved PSO-based method for leak detection and localization in liquid pipelines [J]. IEEE Transactions on Industrial Informatics, 2018, 14(7): 3143–3154. DOI: https://doi.org/10.1109/TII.2018.2794987.
XU Chang-hang, DU Sha-sha, GONG Piao, LI Zhen-xing, CHEN Guo-ming, SONG Gang-bing. An improved method for pipeline leakage localization with a single sensor based on modal acoustic emission and empirical mode decomposition with hilbert transform [J]. IEEE Sensors Journal, 2020, 20(10): 5480–5491. DOI: https://doi.org/10.1109/JSEN.2020.2971854.
HAN Z F, LEUNG C S, SO H C, CONSTANTINIDES A G. Augmented lagrange programming neural network for localization using time-difference-of-arrival measurements [J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3879–3884. DOI: https://doi.org/10.1109/TNNLS.2017.2731325.
DONG Lin-xi, QIAO Zhi-yuan, WANG Hao-nan, YANG Wei-huang, ZHAO Wen-sheng, XU Kui-wen, WANG Gao-feng, ZHAO Li-bo, YAN Hai-xia. The gas leak detection based on a wireless monitoring system [J]. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6240–6251. DOI: https://doi.org/10.1109/TII.2019.2891521.
WANG Xun, LIN Jing-rong, KERAMAT A, GHIDAOUI M S, MENICONI S, BRUNONE B. Matched-field processing for leak localization in a viscoelastic pipe: An experimental study [J]. Mechanical Systems and Signal Processing, 2018, 124: 459–478. DOI: https://doi.org/10.1016/j.ymssp.2019.02.004.
WU Xin, LIU Yi-bing. Leakage detection for hydraulic IGV system in gas turbine compressor with recursive ridge regression estimation [J]. Journal of Mechanical Science and Technology, 2017, 31(10): 4551–4556. DOI: https://doi.org/10.1007/s12206-017-0901-y.
LIU Cui-wei, CUI Zhao-xue, FANG Li-ping, LI Yu-xing, XU Ming-hai. Leak localization approaches for gas pipelines using time and velocity differences of acoustic waves [J]. Engineering Failure Analysis, 2019, 103: 1–8. DOI: https://doi.org/10.1016/j.engfailanal.2019.04.053.
LEE J, CHOI B, KIM E. Novel range-free localization based on multidimensional support vector regression trained in the primal space [J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(7): 1099–1113. DOI: https://doi.org/10.1109/TNNLS.2013.2250996.
MA Da-zhong, WANG Jun-da, SUN Qiu-ye, HU Xu-guang. A novel broad learning system based leakage detection and universal localization method for pipeline networks [J]. IEEE Access, 2020, 7: 42343–42353. DOI: https://doi.org/10.1109/ACCESS.2019.2908015.
HARMOUCHE J, NARASIMHAN S. Long-term monitoring for leaks in water distribution networks using association rules mining [J]. IEEE Transactions on Industrial Informatics, 2020, 16(1): 258–266. DOI: https://doi.org/10.1109/TII.2019.2911064.
ZHOU Zheng, LIN You-zuo, ZHANG Zhong-ping, WU Yue, WANG Zan, DILMORE D, GUTHRIE G. A datadriven CO2 leakage detection using seismic data and spatial-temporal densely connected convolutional neural networks [J]. International Journal of Greenhouse Gas Control, 2019, 90: 1–15. DOI: https://doi.org/10.1016/j.ijggc.2019.102790.
SOHAIB M, KIM J M. Data driven leakage detection and classification of a boiler tube [J]. Applied Science, 2019, 9(12): 2450–2462. DOI: https://doi.org/10.3390/app9122450.
CRUZ R P, SILVA F V, FILETI A M. Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines [J]. Clean Technol Environ Policy, 2020, 22(3): 627–638. DOI: https://doi.org/10.1007/s10098-019-01805-x.
ZHOU Meng-fei, ZHANG Qiang, LIU Yun-wen, SUN Xiao-fang, CAI Yi-jun, PAN Hai-tian. An integration method using kernel principal component analysis and cascade support vector data description for pipeline leak detection with multiple operating modes [J]. Processes, 2019, 7(10): 648–665. DOI: https://doi.org/10.3390/pr7100648.
GUO Yuan, ZENG Yin-chuan, FU Lian-dong, CHEN Xin-yuan. Modeling and experimental study for online measurement of hydraulic cylinder micro leakage based on convolutional neural network [J]. Sensors, 2019, 19(9): 2159–2178. DOI: https://doi.org/10.3390/s19092159.
LANG Xian-ming, LI Ping, CAO Jiang-tao, LI Yan, REN Hong. A small leak localization method for oil pipelines based on information fusion [J]. IEEE Sensors Journal, 2018, 18(15): 6115–6122. DOI: https://doi.org/10.1109/JSEN.2018.2840700.
LIU Cui-wei, LI Yu-xing, XU Ming-hai. An integrated detection and location model for leakages in liquid pipelines [J]. Journal of Petroleum Science and Engineering, 2019, 175: 852–867. DOI: https://doi.org/10.1016/j.petrol.2018.12.078.
YANG Li-jian, WANG Zhu-jun, GAO Song-wei. Pipeline magnetic flux leakage image detection algorithm based on multiscale SSD network [J]. IEEE Transactions on Industrial Informatics, 2020, 16(1): 501–509. DOI: https://doi.org/10.1109/TII.2019.2926283.
LI Shun-ming, WANG Jin-rui, LI Xiang-lian. Theoretical analysis of adaptive harmonic window and its application in frequency extraction of vibration signal [J]. Journal of Central South University, 2018, 25(1): 241–250. DOI: https://doi.org/10.1007/s11771-018-3733-8.
ZHOU Zi-long, CHENG Rui-shan, CHEN Lian-jun, ZHOU Jing, CAI Xin. An improved joint method for onset picking of acoustic emission signals with noise [J]. Journal of Central South University, 2019, 26(10): 2878–2890. DOI: https://doi.org/10.1007/s11771-019-4221-5.
CAI Bao-ping, LIU Yu, XIE Min. A dynamic-bayesian-network-based fault diagnosis methodology considering transient and intermittent faults [J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 276–285. DOI: https://doi.org/10.1109/TASE.2016.2574875.
CAI Bao-ping, LIU Han-lin, XIE Min. A real-time fault diagnosis methodology of complex systems using object-oriented bayesian networks [J]. Mechanical Systems and Signal Processing, 2016, 80: 31–44. DOI: https://doi.org/10.1016/j.ymssp.2016.04.019.
CAI Bao-ping, ZHAO Yu-bin, LIU Han-lin, XIE Min. A data-driven fault diagnosis methodology in three- phase inverters for PMSM drive systems [J]. IEEE Transactions on Power Electronics, 2017, 32: 5590–5600. DOI: https://doi.org/10.1109/TPEL.2016.2608842.
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item
Project(51779267) supported by the National Natural Science Foundation of China; Project(2019YFE0105100) supported by the National Key Research and Development Program of China; Project(tsqn201909063) supported by the Taishan Scholars Project, China; Project(20CX02301A) supported by the Fundamental Research Funds for the Central Universities, China; Project(2019KJB016) supported by the Science and Technology Support Plan for Youth Innovation of Universities in Shandong Province, China
Contributors
The overarching research goals were developed by CAI Bao-ping, LIU Yong-hong and JI Ren-jie. YANG Chao, KONG Xiang-di and TANG An-bang researched the normalization algorithm and carried out experiments to verify it. GAO Chun-tan and LIU Zeng-kai analyzed the calculated results. The initial draft of the manuscript was written by CAI Bao-ping and YANG Chao. All the authors replied to reviewers’ comments and revised the final version.
Conflict of interest
CAI Bao-ping, YANG Chao, LIU Yong-hong, KONG Xiang-di, GAO Chun-tan, TANG An-bang, LIU Zeng-kai and JI Ren-jie declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Cai, Bp., Yang, C., Liu, Yh. et al. A data-driven early micro-leakage detection and localization approach of hydraulic systems. J. Cent. South Univ. 28, 1390–1401 (2021). https://doi.org/10.1007/s11771-021-4702-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-021-4702-1