Abstract
Check valves are key components in hydraulic systems. The cross-port leakage in check valves is a common fault that affects their performances. The vibration and pressure fluctuations excited by leaks are weak, therefore leakage is difficult to be identified and classified by intelligent algorithms and non-destructive testing methods. To maximize the performance of leak pattern recognition, we had improved the sequential minimal optimisation algorithm for enhancing the classification performance and tested it with the University of California Irvine Machine Learning Repository. Furthermore, combining with the search and rescue team (SaRT) algorithm, we propose SaRT-SVM algorithm. Two important parameters γ and C of support vector machine (SVM) were optimised and compared with response surface and other algorithms. We analysed the SaRT–SVM method for leakage pattern recognition and validated the robustness of the developed method by applying the method on multiple fault samples of each fault mode, additional different noises, and another independent data collection. The results showed that the SaRT–SVM algorithm exhibited excellent classification performance and robustness when applied to the leakage pattern recognition of hydraulic check valves under the influence of different noises.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The data that support the findings of this study are available from the corresponding author, Chengbiao Tong cbtong@hunau.edu.cn, upon reasonable request.
Abbreviations
- VA:
-
Variance
- KU:
-
Kurtosis
- SK:
-
Skewness
- RMS:
-
Root mean square
- WF:
-
Wave form factor
- PF:
-
Peaking factor
- IF:
-
Impulse factor
- MF:
-
Margin factor
- GF:
-
Gravity center frequency
- FV:
-
Frequency variance
- MSF:
-
Mean square frequency
- E1–E8 :
-
Energy ratio
- WE:
-
Energy entropy
- EV:
-
Energy variance
References
Ahmadi N, Akbarizadeh G (2020) Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Comput Appl 32(7):2267–2281. https://doi.org/10.1007/s00521-018-3754-0
Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput 10(3):478–495. https://doi.org/10.1007/s12559-017-9542-9
An L, Sepehri N (2005) Hydraulic actuator leakage fault detection using extended Kalman filter. Int J Fluid Power 6(1):41–51. https://doi.org/10.1080/14399776.2005.10781210
Azam MH, Hasan MH, Hassan S, Abdulkadir SJ (2021) A novel approach to generate type-1 fuzzy triangular and trapezoidal membership functions to improve the classification accuracy. Symmetry 13(10):1932. https://doi.org/10.3390/sym13101932
Barrán AT, Alaíz CM, Dorronsoro JR (2021) Faster SVM training via conjugate SMO. Pattern Recognit 111:107644. https://doi.org/10.1016/j.patcog.2020.107644
Cai BP, Fan HY, Shao XY, Liu YH, Liu GJ, Liu ZK, Ji RJ (2020) Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study. Comput Ind Eng 151(1):106983. https://doi.org/10.1016/j.cie.2020.106983
Cao J, Zhang J, Yu X, Tu S-T (2021) Detection of pressure relief valve leakage by tuning generated sound characteristics. Process Saf Environ Prot 148:664–675. https://doi.org/10.1016/j.psep.2021.01.050
Chang S, Shihong Y, Qi L (2020) Clustering characteristics of UCI dataset. In: 2020 39th Chinese control conference (CCC), pp 6301–6306. https://doi.org/10.23919/ccc50068.2020.9189507
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/BF00994018
Dhingra P (2021) Glass identification using extreme gradient boosting algorithm. Int J Sci Res Eng Trends 7(4)
Fu NZ, Huang GY (2022) A fault diagnosis method of check valve based on GADF and prototype network under small samples. Mach Des Res 38(4):132–137. https://doi.org/10.13952/j.cnki.jofmdr.2022.0100
Gao Z, Fang SC, Luo J, Medhin N (2021) A kernel-free double well potential support vector machine with applications. Eur J Oper Res 290(1):248–262. https://doi.org/10.1016/j.ejor.2020.10.040
Guo FY, Zhang YC, Wang Y, Ren P-J, Wang P (2021) Fault diagnosis of reciprocating compressor valve based on transfer learning convolutional neural network. Math Probl Eng 2021:1–13. https://doi.org/10.1155/2021/8891424
Huang W, Liu H, Zhang Y, Mi Y, Shuai B (2021) Railway dangerous goods transportation system risk identification: comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM. Appl Soft Comput 109(5):110–120
Jin Y, Shan C, Wu Y, Xia Y, Zhang Y, Zeng L (2018) Fault diagnosis of hydraulic seal wear and internal leakage using wavelets and wavelet neural network. IEEE Trans Instrum Meas 68(4):1026–1034. https://doi.org/10.1109/tim.2018.2863418
Kauten C, Gupta A, Qin X, Richey G (2021) Predicting blood donors using machine learning techniques. Inf Syst Front 2021:1–16. https://doi.org/10.1007/s10796-021-10149-1
Kim S (2022) Time-domain impedance method for transient analysis and leakage detection in reservoir pipeline valve systems. Mech Syst Signal Process 167:108527. https://doi.org/10.1016/j.ymssp.2021.108527
Kong XD, Cai BP, Liu YH, Zhu HM, Liu YQ, Shao HD, Yang C, Li HJ, Mo TY (2022) Optimal sensor placement methodology of hydraulic control system for fault diagnosis. Mech Syst Signal Process 174:109069. https://doi.org/10.1016/j.ymssp.2022.109069
Kong XD, Cai BP, Liu YH, Zhu HM, Yang C, Gao CT, Liu YQ (2023) Fault diagnosis methodology of redundant closed-loop feedback control systems: subsea blowout preventer system as a case study. IEEE Trans Syst 53(3):3204777. https://doi.org/10.1109/TSMC.2022.3204777
Li CW, Li J, Fang YW (2020) Simulation of the crack geometry effect on the natural vibration frequency of a plate blade. Strength Mater 52(1):97–102. https://doi.org/10.1007/s11223-020-00154-1
Li W, Tong CB, Wu JT, Wu YH (2023) Research on internal leakage prediction in check valve based on multi-source signals. J Electron Meas Instrum 37(1):222–230. https://doi.org/10.13382/j.jemi.B2205771
Liu MZ, Shao YH, Li CN, Chen WJ (2021) Smooth pinball loss nonparallel support vector machine for robust classification. Appl Soft Comput 98:106840. https://doi.org/10.1016/j.asoc.2020.106840
Lu L, Zou J, Fu X (2012) The acoustics of cavitation in spool valve with U-notches. Proc Inst Mech Eng Part G J Aerosp Eng 226(5):540–549. https://doi.org/10.1177/0954410011413221
Namdeo A, Singh D (2021) Challenges in evolutionary algorithm to find optimal parameters of SVM: a review. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.03.288
Paturi UMR, Reddy NS, Cheruku S, Narala SKR, Cho KK, Reddy MM (2021) Estimation of coating thickness in electrostatic spray deposition by machine learning and response surface methodology. Surf Coat Technol 422:127559. https://doi.org/10.1016/j.surfcoat.2021.127559
Selvaraj P, Sarin A, Seraphim BI (2022) Blood donation prediction system using machine learning techniques. Int Conf Comput Commun Inf (ICCCI) 2022:1–4. https://doi.org/10.1109/iccci54379.2022.9740878
Sim HY, Ramli R, Saifizul A, Soong MF (2020) Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique. Meas 152:107315. https://doi.org/10.1016/j.measurement.2019.107315
Sun W, Xu C (2021) Carbon price prediction based on modified wavelet least square support vector machine. Sci Total Environ 754:142052. https://doi.org/10.1016/j.scitotenv.2020.142052
Tong C, Sepehri N, Zhou J (2023) Root cause detection of leakage in check valves using multi-scale signal analysis. J Mech Sci Technol. https://doi.org/10.1007/s12206-022-1207-2
Tran VT, Thobiani F, Tinga T, Ball A, Niu G (2018) Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network. Proc Inst Mech Eng Part C J Mech Eng Sci 232(20):3767–3780. https://doi.org/10.1177/0954406217740929
Trinh M-C, Jun H (2021) Stochastic bending and buckling analysis of laminated composite plates using Latin hypercube sampling. Eng Comput. https://doi.org/10.1007/s00366-021-01544-y
Troß N, Brimmers J, Bergs T (2021) Calculation of the maximum chip thickness for a radial-axial infeed in gear hobbing. Proc CIRP 99:232–236. https://doi.org/10.1016/j.procir.2021.03.032
Wang ZF, He X, Shen H, Fan S, Zeng Y (2022) Multi-source information fusion to identify water supply pipe leakage based on SVM and VMD. Inf Process Manag 59(2):102819. https://doi.org/10.1016/j.ipm.2021.102819
Wang F, Liu Z, Zhou X, Li S, Yuan X, Zhang Y, Shao L, Zhang X (2021a) Oil and gas pipeline leakage recognition based on distributed vibration and temperature information fusion. Result Optic 5:100131. https://doi.org/10.1016/j.rio.2021.100131
Wang Z, Yao L, Chen G, Ding J (2021b) Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals. ISA Trans 114:470–484. https://doi.org/10.1016/j.isatra.2020.12.054
Xu W, Fan S, Wang C, Wu J, Yao Y, Wu J (2022) Leakage identification in water pipes using explainable ensemble tree model of vibration signals. Meas 194:110996. https://doi.org/10.1016/j.measurement.2022.110996
Yao L, Fang Z, Xiao Y, Hou J, Fu Z (2021) An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy 214:118866. https://doi.org/10.1016/j.energy.2020.118866
Yao Z, Yu Y, Yao J (2018) Artificial neural network–based internal leakage fault detection for hydraulic actuators: an experimental investigation. Proc Inst Mech Eng Part I J Syst Control Eng 232(4):369–382. https://doi.org/10.1177/0959651816678502
Ye GY, Xu KJ, Wu WK (2021) Mixed multiple-variable modeling of acoustic emission signals for valve internal leakage detection. IET Sci Meas Technol 15(6):487–498. https://doi.org/10.1049/smt2.12049
Zhang Q, Tao J, Sun Q, Zeng X, Dehmer M, Zhou Q (2021) A fall posture classification and recognition method based on wavelet packet transform and support vector machine. Appl Sci 11(11):5030. https://doi.org/10.3390/app11115030
Zhu SB, Li ZL, Xiang L, Xu HH, Wang XM (2021) Fault diagnosis methodology of redundant closed-loop feedback control systems: subsea blowout preventer system as a case study. Meas 178:109395. https://doi.org/10.1016/j.measurement.2021.109395
Funding
This work was financially supported by Natural Science Foundation of Hunan Province (2020JJ4045); Hunan Province Key R&D Program (2022NK2028); Hunan Agricultural Machinery Equipment R&D Project (X202147); China Scholarship Council.
Author information
Authors and Affiliations
Contributions
Conceptualization: [Nariman Sepehri]; Methodology: [Chengbiao Tong]; Formal analysis and investigation: [Chengbiao tong]; Writing—original draft preparation: [Chengbiao Tong, Nariman Sepehri]; Writing—review and editing: [Chengbiao Tong, Nariman Sepehri]; Funding acquisition: [Chengbiao Tong, Nariman Sepehri], Resources: [Nariman Sepehri, Chengbiao Tong]; Supervision: [Nariman Sepehri].
Corresponding author
Ethics declarations
Conflict of interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tong, C., Sepehri, N. Application of SaRT–SVM algorithm for leakage pattern recognition of hydraulic check valve. Soft Comput 29, 37–51 (2025). https://doi.org/10.1007/s00500-024-10371-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-024-10371-4