Fault diagnosis and test platform for airborne fuel pumps

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Journal of Vibration and Shock ›› 2017, Vol. 36 ›› Issue (1) : 120-133.

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

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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.

Key words

fuel pump / experiment platform / wavelet package analysis / particle swarm optimization / support vector machine

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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

References

[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.