非线性复合 FSVM 在设备故障诊断中的应用

摘要

为了提高设备故障诊断的精度和准确性，利用模糊支持向量机（fuzzy support vector machine，FSVM），处理设备故障样本中的噪声数据。单个核函数构成的FSVM可以处理单一特征的样本集，但不能满足现有设备故障分析实际应用的需求。本文在现有核函数的基础上，进行复合核函数构建，可以有效解决设备故障样本集异构和分布不规则的特征，并可以对于故障进行有效分析，得到较为诊断准确的研究结果。通过对滚动轴承故障实验数据的分析，证明基于复合核函数的FSVM方法，可以有效提高故障诊断的准确度。该方法相比传统FSVM的分析结果，其故障数据信息利用更加充分，故障诊断准确性有效提高，具有较好的有效性和可行性。%A fuzzy support vector machine ( FSVM) is proposed to improve the accuracy of equipment fault diagnosis.FSVM is good at dealing with an equipment fault system or data with ambiguous characters . When equipment fault data have heterogeneous or irregular and uneven distribution characteristics , satis-factory results cannot be obtained by using a traditional FSVM based on single kernel .To overcome this drawback , a FSVM algorithm based on multi-kernel function is proposed to deal with equipment fault diag-nosis.By applying this method to the rolling bearing fault diagnosis , the test result shows that this method is superior to an original FSVM and can identify rolling bearing fault patterns more effectively .