Research on Efficient Verification and State Recognition Method for the Action Reliability of Manual Globe Valve
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摘要: 手动截止阀作为回路系统的典型阀门，是维持系统运行和保护系统安全的重要设备。为高效验证核级手动截止阀的动作可靠性，准确量化地判别其运行状态，本文研究建立了手动截止阀一体化动作试验智能装置，并提出了一种基于小波包分解和支持向量机（SVM）方法相结合的手动截止阀状态识别方法，该方法首先以力矩信号作为特征曲线，利用小波包分解技术提取其时频域特征，融合时域特征构建时域-时频域的混合特征向量；其次采用主成分分析（PCA）方法对特征向量进行降维分析，获取故障特征向量；最后采用支持向量机（SVM）方法对阀门动作状态进行判别。研究结果表明，本研究所建立的装置有效解决了手动截止阀动作可靠性验证耗时长、效率低以及动作过程状态难以量化评估的问题，所提方法能够准确有效地识别阀门的3种动作状态。
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关键词:
- 手动截止阀 /
- 动作可靠性 /
- 状态识别 /
- 小波包分解 /
- 主成分分析（PCA） /
- 支持向量机（SVM）
Abstract: As a typical valve in primary system, manual globe valve is of great importance to maintain system operation and protect system safety. In order to verify the action reliability of the nuclear-grade manual globe valve and determine its operation state accurately and quantitatively, this paper studies and establishes an integrated intelligent operation device for manual globe valve action test, and proposes a method for identifying the state of the manual globe valve based on the combination of wavelet packet decomposition and support vector machine (SVM). Firstly, the torque signal is employed as the characteristic curve and the wavelet packet decomposition technique is utilized to extract the time-frequency domain features. The time domain and time-frequency domain features are integrated to construct the hybrid feature vector. Secondly, the Principal Component Analysis (PCA) is used to perform the dimensionality reduction analysis on the feature vectors to obtain fault feature vectors. Finally, the support vector machine (SVM) method is employed to identify the action state of valve. The results shows that the device constructed in this study solves the problems of long time-consuming and low efficiency in verifying the reliability of manual globe valve actions, as well as the difficulty in quantifying the evaluation of the action process. The proposed method can identify the three action states of the valve accurately and efficiently. -
图 1 手动截止阀一体化动作试验智能装置结构示意图
Figure 1. Schematic Diagram of Intelligent Testing System for Manual Globe Valve
图 2 不同动作状态下的阀门力矩信号曲线
Figure 2. Torque Signal Curves for Valve under Different Action States
图 3 不同特征指标对不同动作状态类型的主元分析结果
Figure 3. PCA of Different Action States Using Various State Indexes
图 6 特征向量指标训练集及测试集状态识别结果
Figure 6. State Recognition Results of Feature Vector Indicator Training Set and Test Set
表 1 试验效率对比
Table 1. Comparison of Test Efficiency
试验装置 试验周期/d 人力投入/(人·d−1) 传统手动驱动装置 62 5 本文装置 36 1 
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表 2 各主元贡献率
Table 2. Contribution Rate of Each Principle Component
主元序号 贡献率/% 主元序号 贡献率/% 1 77.56 7 0.38 2 10.72 8 0.34 3 6.29 9 0.16 4 1.92 10 0.08 5 1.41 11 0.08 6 0.93 12 0.04
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表 3 SVM参数设置情况
Table 3. Parameter Setting of Support Vector Machine
参数 设置情况 模型设置类型 C-SVM 核函数类型 径向基函数 交互检验模式 留一法交叉验证 惩罚参数 1 Gamma参数 1/3
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[1] 魏国俭,陶瑞峰,许健,等. 航天阀门运动副卡滞故障分析及对策[J]. 航天器环境工程,2012, 29(1): 7-13. doi: 10.3969/j.issn.1673-1379.2012.01.002 [2] 尚群立,李梦强,张晶瑜. 气动截止阀机理建模及其在阀门故障诊断中的应用[J]. 浙江工业大学学报,2020, 48(2): 154-158,216. doi: 10.3969/j.issn.1006-4303.2020.02.006 [3] 邱金水,李少杰,刘少刚,等. 舰船特种阀门极少失效条件下可靠性寿命研究[J]. 哈尔滨工程大学学报,2012, 33(9): 1086-1090. [4] 王洪波,黄智鹏,柳志姣,等. 基于故障模式及失效机理分析的电液伺服阀寿命分析[J]. 机电工程,2022, 39(8): 1132-1137. doi: 10.3969/j.issn.1001-4551.2022.08.015 [5] AL-RAHEEM K F, ROY A, RAMACHANDRAN K P, et al. Application of the Laplace-wavelet combined with ANN for rolling bearing fault diagnosis[J]. Journal of Vibration and Acoustics, 2008, 130(5): 051007. doi: 10.1115/1.2948399 [6] 孙伟,熊邦书,黄建萍,等. 小波包降噪与LMD相结合的滚动轴承故障诊断方法[J]. 振动与冲击,2012, 31(18): 153-156. [7] MISHRA C, SAMANTARAY A K, CHAKRABORTY G. Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising[J]. Measurement, 2017, 103: 77-86. doi: 10.1016/j.measurement.2017.02.033 [8] KANKAR P K, SHARMA S C, HARSHA S P. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform[J]. Neurocomputing, 2013, 110: 9-17. doi: 10.1016/j.neucom.2012.11.012 [9] VAPNIK V N. Statistical learning theory[J]. Encyclopedia of the Sciences of Learning, 2010, 41(4): 3185. [10] 姜久亮,刘文艺,侯玉洁,等. 基于内积延拓LMD及SVM的轴承故障诊断方法研究[J]. 振动与冲击,2016, 35(6): 104-108. [11] KONAR P, CHATTOPADHYAY P. Bearing fault detection of induction motor using wavelet and support vector machines (SVMs)[J]. Applied Soft Computing, 2011, 11(6): 4203-4211. doi: 10.1016/j.asoc.2011.03.014 [12] BOURINET J M, DEHEEGER F, LEMAIRE M. Assessing small failure probabilities by combined subset simulation and support vector machines[J]. Structural Safety, 2011, 33(6): 343-353. doi: 10.1016/j.strusafe.2011.06.001 -
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