QAPSO-BP算法及其在水电机组振动故障诊断中的应用

摘要
图/表
参考文献(18)
相关文章 (15)

全文: PDF (976 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对水电机组振动故障耦合因素多、故障模式复杂等问题，提出了一种基于量子自适应粒子群优化BP神经网络（QAPSO-BP）的故障诊断模型。在QAPSO-BP算法中，利用量子计算中的叠加态特性和概率表达特性，增加了种群的多样性；根据各粒子的位置与速度信息，实现惯性因子的自适应调节；为避免陷入局部最优，在算法中加入变异操作；并以此来训练BP神经网络，实现网络的参数优化，进而构建了机组的振动故障诊断模型。仿真实例表明，与粒子群优化BP网络（PSO-BP）法和BP网络法相比，该算法具有较高的诊断准确度，适用于水电机组振动故障的模式识别。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	程加堂
	段志梅
	熊燕

关键词 ： BP神经网络, 量子自适应粒子群优化算法, 水电机组, 振动, 故障诊断

Abstract：For the problems of vibration fault with many coupling factors and complex fault modes of hydroelectric generating unit, a method of quantum adaptive particle swarm optimized BP neural network (QAPSO-BP) was proposed. In this algorithm, The characteristics of superposition state and probability expression in the quantum computing was adopted to increase the diversity of population. The position and velocity information of each particle was used to adjust inertia factor adaptively. To avoid falling into local optimum, mutation process was added in the approach. Afterwards, the BP neural network was trained with QAPSO to achieve the optimization parameters, then the vibration fault diagnosis model was established. The simulation shows that the diagnostic accuracy is higher of the QAPSO-BP algorithm than those by particle swarm optimized BP network (PSO-BP) and BP neural network, and is suitable for fault modes recognition of hydroelectric generator unit.

Key words： BP neural network quantum adaptive particle swarm optimization (QAPSO) hydroelectric generating unit vibration fault diagnosis

收稿日期: 2014-11-19 出版日期: 2015-12-15

引用本文:

程加堂，段志梅，熊燕. QAPSO-BP算法及其在水电机组振动故障诊断中的应用[J]. 振动与冲击, 2015, 34(23): 177-181.
CHENG Jiatang，DUAN Zhimei，XIONG Yan. QAPSO-BP and its application in vibration fault diagnosis for hydroelectric generating unit. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(23): 177-181.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2015/V34/I23/177

[1] 张彼德，田源，邹江平，等.基于Choquet模糊积分的水电机组振动故障诊断[J].振动与冲击，2013，32（12）:61-66.
ZHANG Bide, TIAN Yuan, ZOU Jiangping, et al. Vibration fault diagnosis of a Hydro-generating unit based on choquet fuzzy integration [J]. JOURNAL OF VIBRATION AND SHOCK, 2013, 32(12): 61-66.
[2] Zhang Xiaoyuan, Zhou Jianzhong, Guo Jun, et al. Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine [J] . Expert Systems With Applications，2012，39(3)：2621-2628.
[3] 易辉，梅磊，李丽娟，等.基于多分类相关向量机的水电机组振动故障诊断[J].中国电机工程学报，2014，34（17）:2843-2850.
YI Hui, MEI Lei, LI Lijuan, et al. Vibration Fault Diagnosis for Hydroelectric Generating Units Using the Multi-class Relevance Vector Machine [J]. Proceedings of the CSEE, 2014, 34(17): 2843-2850.
[4] 张孝远，周建中，王常青，等.考虑样本交叠的水电机组振动故障诊断[J].电力系统保护与控制，2012，40（3）:8-14.
ZHANG Xiaoyuan, ZHOU Jianzhong, WANG Changqing, et al. Vibrant fault diagnosis for hydroelectric generator unit considering overlapping fault patterns [J]. Power System Protection and Control, 2012, 40(3):8-14.
[5] 贾嵘，洪刚，武桦，等. 基于IPSO优化LSSVM的水轮发电机组振动故障诊断[J].水利学报，2011，42（3）:373-378.
JIA Rong, HONG Gang, WU Hua, et al. Vibration fault diagnosis of hydroelectric generating unit by Least Squares Support Vector Machine based on Improved Particle Swarm Optimization [J]. SHUILI XUEBAO, 2011, 42(3): 373-378.
[6] 郭鹏程，孙龙刚，李辉，等.基于多重分形谱和改进BP神经网络的水电机组振动故障诊断研究[J].水力发电学报，2014，33（3）:299-305.
GUO Pengcheng, SUN Longgang, LI Hui, et al. Vibration fault diagnosis of hydropower unit based on multi-fractal spectrum and improved BP neural network [J]. JOURNAL OF HYDROELECTRIC ENGINEERING, 2014, 33(3): 299-305.
[7] 李鑫，王少萍.基于卡箍优化布局的飞机液压管路减振分析[J].振动与冲击，2013，32（1）:14-20.
LI Xin, WANG Shaoping. Vibration control analysis for hydraulic pipelines in an aircraft based on optimized clamp layout [J]. JOURNAL OF VIBRATION AND SHOCK, 2013, 32(1): 14-20.
[8] 张海鹏，韩端锋，郭春雨.改进PSO训练的BPNN方法的舰船主尺度建模[J].哈尔滨工程大学学报，2012，33（7）:806-810.
ZHANG Haipeng, HAN Duanfeng, GUO Chunyu. Modeling of the principal dimensions of large vessels based on a BPNN trained by an improved PSO [J]. Journal of Harbin Engineering University, 2012, 33(7): 806-810
[9] 张晓辉，董兴华.含风电场多目标低碳电力系统动态经济调度研究[J].电网技术，2013，37（1）:24-31.
ZHANG Xiaohui, DONG Xinghua. Research on Multi-Objective Scheduling for Low-Carbon Power System With Wind Farms [J]. Power System Technology, 2013, 37(1): 24-31.
[10] LIANGkui Lin, HUI Xu, DAN Xu, et al. QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation [J].Journal of Systems Engineering and Electronics，2011,22(3):405-411.
[11] 陈道君，龚庆武，金朝意，等.基于自适应扰动量子粒子群算法参数优化的支持向量回归机短期风电功率预测[J].电网技术，2013，37(4)：974-980.
CHEN Daojun, GONG Qingwu, JIN Zhaoyi, et al. Short-Term Wind Power Prediction Based on Support Vector Regression Machine Optimized by Adaptive Disturbance Quantum-Behaved Particle Swarm Optimization [J]. Power System Technology, 2013, 37(4): 974-980.
[12] 刘立峰，孙赞东，韩剑发，等.量子粒子群模糊神经网络碳酸盐岩流体识别方法研究[J].地球物理学报，2014，57(3)：991-1000.
LIU Lifeng, SUN Zandong, HAN Jianfa, et al. A carbonate fluid identification method based on quantum particle swarm fuzzy neural network [J]. CHINESE JOURNAL OF GEOPHYSICS, 2014, 57(3): 991-1000.
[13] 郭通，兰巨龙，李玉峰，等.基于量子自适应粒子群优化径向基函数神经网络的网络流量预测[J].电子与信息学报，2013，35(9)：2220-2226.
Guo Tong, Lan Julong, Li Yufeng, et al. Network Traffic Prediction with Radial Basis Function Neural Network Based on Quantum Adaptive Particle Swarm Optimization [J]. Journal of Electronics & Information Technology, 2013, 35(9): 2220-2226.
[14] 陈义雄，梁昔明，黄亚飞.基于佳点集构造的改进量子粒子群优化算法[J].中南大学学报(自然科学版)，2013，44(4)：1409-1414.
CHEN Yixiong , LIANG Ximing, HUANG Yafei. Improved quantum particle swarm optimization based on good-point set [J]. Journal of Central South University (Science and Technology), 2013, 44(4): 1409-1414.
[15] 卢娜，肖志怀，符向前.基于蚁群初始化小波网络的水电机组振动故障诊断[J].水力发电学报，2014，33（2）:251-258.
LU Na, XIAO Zhihuai, FU Xiangqian. Vibration fault diagnosis of hydroelectric generating unit based on ACO-initialized wavelet network [J]. OURNAL OF HYDROELECTRIC ENGINEERING, 2014, 33(2): 251-258.
[16] 李超顺，周建中，肖剑，等.基于引力搜索核聚类算法的水电机组振动故障诊断[J].中国电机工程学报，2013，33（2）:98-104.
LI Chaoshun, ZHOU Jianzhong, XIAO Jian, et al. Vibration Fault Diagnosis of Hydroelectric Generating Unit Using Gravitational Search Based Kernel Clustering Method [J]. Proceedings of the CSEE, 2013, 33(2): 98-104.
[17] 肖剑，周建中，张孝远，等. 基于Levy-ABC优化SVM的水电机组故障诊断方法[J].振动、测试与诊断，2013，33（5）:839-844.
XIAO Jian, ZHOU Jianzhong, ZHANG Xiaoyuan, et al. Fault diagnosis for Hydroelectric Generator Unit Based on Levy Flight-Artificial Bee Colony and Support Vector Machine [J].Journal of Vibration, Measurement & Diagnosis, 2013, 33(5): 839-844.
[18] 卢娜，肖志怀，曾洪涛，等.基于径向基多小波神经网络的水电机组故障诊断[J].武汉大学学报（工学版），2014，47(3)：388-393.
LU Na, XIAO Zhihuai, ZENG Hongtao, et al. Fault diagnosis of hydro-turbine generating unit based on radial basis multiwavelet [J].Engineering Journal of Wuhan University, 2014, 47(3): 388-393.