摘要
为了克服在无先验知识的情况下,人为选择时域无量纲指标作为故障敏感特征的盲目性,提出了一种基于特征评估和径向基函数(RBF)神经网络的机械故障诊断模型.该模型分别采用小波包和经验模式分解方法对原始振动信号进行分解,分别提取原始信号和各分解信号的时域无量纲指标组成联合特征,然后对联合特征进行评估,计算评估因子,并根据评估因子的大小选取敏感特征作为RBF神经网络的输入,实现对机器不同状态的自动识别.实验结果和工程应用表明,这种集成了小波包、经验模式分解、特征评估方法和RBF神经网络的机械故障诊断模型能够精细地获取故障信息,从大量的故障特征中筛选出敏感特征,因而减小了网络规模,提高了分类准确率,具有很强的鲁棒性.
To overcome blindness of subjective selecting dimensionless indicators as sensitive features without any experience, a novel mechanical fault diagnosis model based on feature evaluation and radial basis function （RBF） networks is proposed, where the original signals are decomposed via wavelet packet and empirical mode decomposition （EMD） respectively, and the dimensionless indicators in time domain are extracted from the original signals and each decomposed signal to construct the combined features. Furthermore, a feature evaluation method is applied to calculate evaluation factors of the combined feature.s, and the corresponding sensitive features are selected according to the evaluation factors and input into the RBF networks to automatically identify different conditions of mechanical equipment. The experiments of rolling bearings fault diagnosis are carried out to test the performance of this model. The results demonstrate that the model integrating wavelet packet, EMD, feature evaluation method and RBF networks enables to precisely extract fault information, and select sensitive ones from a large number of features to correctly and rapidly diagnose the mechanical faults. This model is also employed to classify heavy oil catalytic cracking set under 3 conditions. The results show it can reduce the networks scale, increase the classification accuracy, and enhance the robustness.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2006年第5期558-562,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金重点资助项目(50335030)
国家重点基础研究发展计划资助项目(2005CB724106)
西安市科技攻关计划资助项目(GG050410)
关键词
特征评估
小波包
经验模式分解
径向基函数神经网络
故障诊断模型
feature evaluation
wavelet packet
empirical mode decomposition
radial basis function networks
fault diagnosis model
