Abstract：Aiming at the problem of inaccurate regression modeling in the current research on acoustic emission detection of valve leakage, considering the practical application needs, a classification modeling study of valve liquid leakage is carried out. The mechanism and basic characteristics of valve leakage acoustic emission are analyzed, and a support vector machine(SVM) classification model for valve leakage acoustic emission signal feature quantity and leakage level is established. The valve leakage acoustic emission signal collection experiment is performed at the industrial production site, and the collected signals are pre-processed and feature extracted. The grid search method is used to find the optimal training parameters, and the optimal support vector machine classification model is established. The model prediction results show that the accuracy of valve leakage model prediction ang identification is more than 93%.
［1］方学锋, 梁华, 夏志敏, 等. 基于声发射技术的阀门泄漏在线检测方法 ［J］. 化工机械, 2007, 34 (1): 52-54.
Fang X F, Liang H, Xia Z M, et al. On-line detection method of valve leakage based on acoustic emission technology ［J］. Chemical Machinery, 2007, 34 (1): 52-54.
［2］Lee J H, Lee M R, Kim J T, et al．Analysis of acoustic emission signals for condition monitoring of check valve at nuclear power plants ［J］. Key Engineering Materials, 2004, 270-273 (1-3): 531-536.
［3］Lee S G, Park J H, Yoo K B, et al．Evaluation of internal leak in valve using acoustic emission method ［J］. Key Engineering Materials, 2006,326-328 (1-2): 661-664.
［4］Lee J H, Lee M R, Kim J T, et al. A study of the characteristics of the acoustic emission signals for condi-tion monitoring of check valves in nuclear power plants ［J］. Nuclear Engineering and Design, 2006, 236 (13): 1411-1421.
［5］Lamonaca F, Carrrozzini A, Grimaldi D, et al. Improved monitoring of acoustic emissions in concrete structures by multi-triggering and adaptive acquisition time interval ［J］. Measurement, 2015,59 (10): 227-236.
［6］Kaewwaewnoi W, Prateepasen A, Kaewtra-kulponng P. Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage ［J］. Measurement, 2010, 43 (2): 274-282.
［7］Ye G Y, Xu K J, Wu W K. Standard deviation based acoustic emission signal analysis for detecting valve internal leakage ［J］. Sensors and Actuators A: Physical, 2018, 283: 340-347.
［8］Li Z L, Zhang H F, Tan D J, et al. A novel acoustic emission detection module for leakage recognition in a gas pipeline valve ［J］. Process Safety & Environmental Protection, 2017, 105: 32-40.
［9］Zhu S B, Li Z L, Zhang S M, et al. Natural gas pipeline valve leakage rate estimation via factor and cluster analysis of acoustic emissions ［J］. Measurement, 2018, 125: 48-55.
［10］ASTM. E1316—1996 Terminology for Nondestructive Examination ［S］. 1996.
［11］耿荣生, 沈功田, 刘时风. 声发射信号处理和分析技术 ［J］. 无损检测, 2002, (1): 23-28.
［12］Noipitak M, Prateepasen A, Kaewwaewnoi W. A relative calibration method for a valveleakage rate measurement system ［J］. Measurement, 2011,44(1): 211-218.
［13］张沫,郑慧峰,倪豪,等. 基于遗传算法优化支持向量机的超声图像缺陷分类［J］. 计量学报, 2019, 40(5): 887-892.
Zhang M, Zheng H F, Ni H, et al. Ultrasonic Image Defect Classification Based on Support Vector Machine Optimized by Genetic Algorithm［J］. Acta Metrologica Sinica, 2019, 40(5): 887-892.
［14］Sun L Y, Li Y B, Liu T G, et al.Leak Detection and Position Method for Pressure Piping Using Acoustic Emission［C］// 2008 7th World Congress on Intelligent Control and Automation. Chongqing, China, 2008: 8419-8424.
［15］Silva L R, Deschamps C J. Modeling of gas leakage through compressor valves ［J］. International Journal of Refrigeration, 2015, 53: 195-205.
［16］Rodrigo L, Cesar J D. Numerical modeling of startup and shutdown transients in reciprocating com-pressors ［J］. International Journal of Refrigeration, 2011, 34 (6): 1398-1414.
［17］Nivesrangsan P, Steel J A, Reuben R L. Acoustic emission mapping of diesel engines for spatially located time series-part II: spatial reconstitution ［J］. Mechanical Systems and Signal Processing, 2007,21 (2): 1084-1102.
［18］郭保苏, 吴文文, 付强, 等. 基于支持向量机分类策略的多晶硅电池片色差检测 ［J］. 计量学报, 2019, 40 (6): 1013-1019.
Guo B S, Wu W W, Fu Q, et al. Color difference detection of polysilicon cells based on support vector machine classification strategy ［J］. Acta Metrologica Sinica, 2019, 40 (6): 1013-1019.
［19］侯伟真. 求解支持向量机的若干优化算法的研究 ［D］. 青岛: 山东科技大学, 2007.
2021, Vol. 42 
