基于PSODACCIW-VPMCD的滚动轴承智能检测方法

PDF(1121 KB)

振动与冲击 ›› 2015, Vol. 34 ›› Issue (23) : 42-47.

论文

刘吉彪1，程军圣2，马利2

作者信息 +

A intelligent detection method for rolling bearing Based on WCPSO-VPMCD

LIU Jibiao1，Cheng Junsheng2,Ma Li2

Author information +

文章历史 +

摘要

针对VPMCD中模型选择方法的不合理和小样本多分类时识别率降低的缺陷，结合动态加速常数协同惯性权重的粒子群 (Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight, WCPSO) 算法的全局优化能力和加权融合理论，提出基于WCPSO-VPMCD的滚动轴承智能检测方法。首先对样本提取特征变量，然后采用WCPSO算法优化诊断融合权值矩阵，最后对滚动轴承的故障类型和工作状态进行分类和识别。实验结果表明，该方法能够有效地应用于滚动轴承的智能检测中。

Abstract

Aiming at the unreasonable model selection method and the defect of lower recognition rate under the condition of smaller samples and multi-classification, combined with the global optimization ability of Particle swarm optimization with dynamic accelerating constant and coordinating with inertia weight (WCPSO) algorithm and weight fusion theory, an intelligent detection method for rolling bearing Based on WCPSO-VPMCD has been put forward. Firstly, characteristic variables of the samples are extracted, then WCPSO algorithm is used to optimize diagnosis combination weight matrix, finally, the work states and faults pattern of the rolling bearing can be classified and identified. The experimental results show that the method can be applied to rolling bearing intelligent detection effectively.

导出引用

刘吉彪1，程军圣2，马利2. 基于PSODACCIW-VPMCD的滚动轴承智能检测方法[J]. 振动与冲击, 2015, 34(23): 42-47

LIU Jibiao1，Cheng Junsheng 2,Ma Li2. A intelligent detection method for rolling bearing Based on WCPSO-VPMCD[J]. Journal of Vibration and Shock, 2015, 34(23): 42-47

参考文献

[1] 温熙森.模式识别与状态监控[M].北京:科学出版社,2007:23-39.
Wen Xisen.Pattern Recognition and Condition Monitoring [M].Beijing: Science Press, 2007:23-39.
[2] WANG Huaqing, CHEN Peng. Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network [J]. Computer & Industrial Engineering, 2011, 60(4): 511-518.
[3] FEI Shengwei, ZHANG Xiaobin.Fault diagnosis of power transformer based on support vector machine with genetic algorithm [J]. Expert Systems with Applications, 2009, 36(8):11352-11357.
[4] Raghuraj R, Lakshminarayanan S.Variable predictive models-A new multivariate classification approach for pattern recognition applications [J].Pattern Recognition, 2009, 42(1):7-16.
[5] Raghuraj R, Lakshminarayanan S.Variable predictive model based classification algorithm for effective separation of protein structural classes [J].Computational Biology and Chemistry, 2008, 32(4):302-306.
[6] WANG Huaqing, CHEN Peng. Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network [J]. Computer & Industrial Engineering, 2011, 60(4): 511-518.
[7] 刘芬,潘宏侠.WCPSO优化的小波神经网络在传动箱故障诊断的应用[J].噪声与振动控制,2011,05:146-149.
LIU Fen, PAN Hongxia.Study on Application of WCPSO Optimizing Wavelet Neural Network for Gear Box Fault Diagnosis [J]. Noise and Vibration Control, 2011, 05:146-149.
[8] Cosmin Danut, Bocaniala, JoséL Sa da Costa.Tuning the parameters of a classifier for fault diagnosis-particle swarm optimization vs genetic algorithms [J].ICINCO (1)2004:157-162.
[9] EUNJU KIM, WOOJU KIM, YILLBYUNG LEE. Combination of multiple classifiers for the customer's purchase behavior prediction [J]. Decision Support Systems 2002, 34: 167- 175.
[10] 刘占生,窦唯,王东华.基于遗传算法的旋转机械故障诊断方法融合[J].机械工程学报,2007,43(10):227- 233.
LIU Zhansheng,DOU Wei,Wang Donghua.Rotating machinery fault diagnosis combination of method based on genetic algorithm[J].Journal of Mechanical Engineering,2007,43(10): 227-233.
[11] 魏秀业,潘宏侠.粒子群优化及智能故障诊断[M].北京:国防工业出版社，2010:26-41.
Wei Xiuye, Pan Hongxia. Particle swarm optimization and intelligent fault diagnosis [M].Beijing, National Defense Industry Press, 2010:26-41.
[12] 程军圣，郑近德，杨宇.一种新的非平稳信号分析方法——局部特征尺度分解法[J]. 振动工程学报，2012,25(2):215-220.
CHENG Junsheng, ZHENG Jinde, YANG Yu.A nonstationary signal analysis approach——the local characteristic—scale decomposition method [J]. Journal of Vibration Engineering, 2012, 25(2):215-220.
[13] Jiang Yonghua, Tang Baoping, QinYi, etal.Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD [J]. Renewable Energy, 2011, 36(8): 2146-2153.