摘要
为了解决在机械智能监测与诊断中缺少故障样本的问题,提出了一种机械故障单值分类的新方法———支持向量数据描述法.该方法只需要一类目标样本作为学习样本,而不需要其他非目标样本,就可以建立起单值分类器,从而区分了非目标样本和目标样本.将这种方法应用在机械故障诊断和状态监测中,仅仅依靠正常运行时的数据信号,而不需要故障数据,就可以监测机器的运行状态,且不需要对原始数据进行特征提取.实验结果表明,支持向量数据描述法与传统的神经网络方法相比,具有较好的分类能力和较高的计算效率.
In order to solve the problem of insufficient fault samples in intelligent monitoring and diagnosis for machinery, a new method of one-class classification of mechanical faults-support vector data description is proposed. With this method, the outlier objects can be distinguished from target objects if the information of the target class is available without knowing the outlier class. Applying this method to mechanical condition monitoring and fault diagnosis, machine condition can be monitored only by using normal condition signals. It is unnecessary for this method to preprocess the signals to extract their features. The experimental results show that support vector data description method has stronger classification ability and higher efficiency than conventional classification method of neural network.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2003年第9期910-913,共4页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(50175087).
关键词
支持向量数据描述
单值分类
故障诊断
Classification (of information)
Data description
Learning systems
Mechanisms
Neural networks
Sampling
