摘要
为了提高滚动轴承故障诊断的准确性和适应性,提出快速独立成分分析(fast independent component analysis,FICA)和增量概率神经网络(incremental probabilistic neural network,IPNN)相结合的FICA-IPNN集合型滚动轴承故障诊断方法。首先,针对滚动轴承的故障振动信号非高斯特点,利用固定点迭代的FICA算法提取出滚动轴承振动信号特征,其次,为了提高概率神经网络分类的适应性,采用在线增量方法,优化概率神经网络结构,训练概率神经网络参数。实验表明,该集合型故障诊断方法较传统概率神经网络有更高的分类准确性和适应性。
An ensemble approach based on fast independent component analysis （FICA） and incremental probabilistic neural network, called FICA-IPNN, was proposed to improve the accuracy and adaptability of rolling bearing fault diagnosis. Firstly, the feature of the vibration signals of the rolling bearing, usually non-Gaussian, was extracted by the fixed-point iteration FICA algorithm. Then the online incremental method was adopted to optimize the probabilistic neural network structure and train probabilistic neural network parameters to improve the classification adaptability of probabilistic neural network. The experimental results show that the accuracy and adaptability of classification by FICA-IPNN are better than that of traditional probabilistic neural network.
出处
《电机与控制学报》
EI
CSCD
北大核心
2014年第3期73-78,共6页
Electric Machines and Control
基金
国家自然科学基金(60974070)
辽宁省科学技术计划项目(2010222005)
关键词
故障诊断
快速独立成分分析
增量概率神经网络
特征提取
滚动轴承
fault diagnosis
fast independent component analysis
incremental probabilistic neural network
feature extract
rolling element bearing
