机载燃油泵故障诊断及实验平台研究

PDF(3977 KB)

振动与冲击 ›› 2017, Vol. 36 ›› Issue (1) : 120-133.

论文

机载燃油泵故障诊断及实验平台研究

焦晓璇1，景博1，羌晓清1，刘晓东3，4，李娟1，2，周伟1

作者信息 +

Fault diagnosis and test platform for airborne fuel pumps

JIAO Xiao-xuan1, JING Bo1, QIANG xiao-qing1, LIU Xiao-dong3,4, Li Juan1,2 ,Zhou Wei1

Author information +

文章历史 +

摘要

以机载燃油泵实时状态监测为背景，针对目前燃油泵故障数据少、诊断方法效率低、成本高等问题，研制了机载燃油转输系统实验平台，提出基于小波包分析和改进粒子群支持向量机（M-PSO-SVM）的故障诊断方法。该实验平台可针对燃油泵5种典型故障模式进行实验，测取泵故障状态下的振动信号和出口压力信号。利用小波包分解提取振动信号不同频段的能量值作为特征参数，并结合出口压力均值构造故障特征向量。提出混合遗传变异思想的粒子群算法对SVM分类模型进行参数优化，用得到的故障特征向量训练并验证该分类模型。实验分析表明，该实验平台可有效采集泵的故障信号，并且测试点可进一步优化，M-PSO-SVM在诊断速度、诊断精度等方面都优于传统Grid-SVM和GA-SVM，能够满足实际故障诊断的需求。

Abstract

Under the background of realtime status monitoring for airborne fuel pumps, aiming at lack of fault data and efficiency, and highcost of now available fault diagnosis methods, a test platform of a fuel transfer system was developed and a fault diagnosis method based on wavelet packet analysis, modified particle swarm optimization and support vector machine (M-PSO-SVM) was proposed. The test platform could run tests for five typical fault modes of fuel pumps to acquire vibration signals and outlet pressure signals under malfunction conditions. The energy of different frequency bands of vibration signals extracted with the wavelet packet decomposition was regarded as characteristic parameters to construct fault feature vectors combined with the mean outlet pressures. The particle swarm optimization algorithm with the thought of genetic variation was presented to optimize the parameters of a SVM classification model. Meanwhile, the fault feature vectors were used to train and validate this classification model. The examples demonstrated that the test platform is quite effective to get fault signals of fuel pumps and the measurement points can be further optimized; the M- PSO-SVM has higher performances than Grid-SVM and GA-SVM do and it can meet the requirements of practical fault diagnosis.

导出引用

焦晓璇1，景博1，羌晓清1，刘晓东3，4，李娟1，2，周伟1. 机载燃油泵故障诊断及实验平台研究[J]. 振动与冲击, 2017, 36(1): 120-133

JIAO Xiao-xuan1, JING Bo1, QIANG xiao-qing1, LIU Xiao-dong3,4, Li Juan1,2,Zhou Wei1. Fault diagnosis and test platform for airborne fuel pumps[J]. Journal of Vibration and Shock, 2017, 36(1): 120-133

参考文献

[1] Muralidharan V, Sugumaran V. Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump[J]. Measurement, 2013, 46(9): 3057 -3063.
[2] Zhang X, Tang L, Decastro J. Robust fault diagnosis of aircraft engines: a nonlinear adaptive estimation-based approach [J]. IEEE Transactions on Control Systems Technology, 2013, 21 (3) :861-868.
[3] 段向阳，王永生，苏永生. 振动分析在离心泵空化监测中的应用[J]. 振动与冲击, 2011, 30(4): 161-165.
Duan Xiang-yang, Wang Yong-sheng, Su Yong-sheng. Vibration Analysis of Cavitation Monitoring in Centrifugal Pump[J]. Journal of Vibration and Shock, 2011, 30(4): 161-165.
[4] 王涛，李艾华，王旭平，等. 基于SVDD和距离测度的齿轮泵故障诊断方法研究[J]. 振动与冲击, 2013, 32(11): 62-65.
Wang Tao, Li Aihua, Wang Xu-ping, et al. Study on Fault Diagnosis Method for Gear Pump Based on Support Vector Domain Description and Distance Measure[J]. Journal of Vibration and Shock, 2013, 32(11): 62-65.
[5] Hancock K M, ZHANG Q. A hybrid approach to hydraulic vane pump condition monitoring and fault detection[J]. Transactions of the ASABE, 2006, 49(4)：1203-1211.
[6] GAO Y, ZHANG Q, KONG X. Comparison of hydraulic pump faults diagnosis methods：Wavelet vs.spectral analyses[C]. ASME 2005 International Mechanical Engineering Congress and Exposition：73-78.
[7] 王杰华. 基于BP神经网络的离心油泵故障诊断研究[D]. 河北，河北工程大学，2013.
Wang Jie-hua. Fault diagnosis of centrifugal oil pump based on BP neural network[D]. He Bei, Hebei University of Engineering, 2013.
[8] Muralidharan V, V Sugumaran, V Indira. Fault diagnosis of monoblock centrifugal pump using SVM[J]. Engineering Science and Technology An International Journal, 2014. 17(3): 152-157.
[9] 洪涛，黄志奇，杨畅. 涡轮泵实时故障检测的快速支持向量机算法[J]. 仪器仪表学报, 2012,33(8):1786-1792.
Hong Tao, Huang Zhi-qi, Yang Chang. Fast support vector machine algorithm forturbopump real-time fault detection[J]. Chinese Journal of Scientific Instrument, 2012,33(8):1786-1792.
[10] 徐玉秀，杨文平，吕轩，等. 基于支持向量机的汽车发动机故障诊断研究[J]. 振动与冲击, 2013, 32(8): 143-146.
Xu Yu-xiu, Yang Wen-ping, Lv Xuan, et al. Study on fault diagnosis of car engine Based on Support Vector Machine[J]. Journal of Vibration and Shock, 2013, 32(8): 143-146..
[11] Huanhuan Chen, Qiang Wang, Yi Shen. Decision tree support vector machine based on genetic algorithm for multi-class classification[J]. Journal of Systems Engineering and Electronics, 2011,2 (4):322-326.
[12] Kezong Tang, Jingyu Yang, Haiyan Chen, Shang Gao. Improved genetic algorithm for nonlinear programming problems[J]. Journal of Systems Engineering and Electronics, 2011,22(3):540-546.
[13] 宋小杉，蒋晓瑜，罗建华，等. 基于类间距的径向基函数-支持向量机核参数评价方法分析[J].兵工学报,2012,33(2):203-208.
SONG Xiao-shan, JIANG Xiao-yu, LUO Jian-hua, et al. Analysis of the Inter-class Distance-based Kernel Parameter Evaluating Method for RBF-SVM[J]. Acta Armamentarii, 2012, 33(2):203-208.
[14] 王维刚，刘占生. 多目标粒子群优化的支持向量机及其在齿轮故障诊断中的应用[J]. 振动工程学报, 2013,26 (5):743-749.
WANG Wei-gang, LIU Zhan-sheng. Support vector machine optimized by multi-objective particle swarm and application in gear fault diagnosis[J]. Journal of Vibration Engineering, 2013,26 (5):743-749.
[15] Li K, Gao X, Tian Z, et al. Using the curve moment and the PSO-SVM method to diagnose downhole conditions of a sucker rod pumping unit[J].Petroleum Science, 2013, 10 (1) :73-80.