基于在线半监督学习的故障诊断方法研究

针对机械故障诊断中准确、完备的故障训练样本获取困难,而现有分类方法难以有效地发掘大量未标记故障样本中蕴含的有用信息,提出了一种基于在线半监督学习的故障诊断方法.该方法基于Tri-training算法将在线贯序极限学习机从监督学习模式扩展到半监督学习模式,利用少量不精确的标记样本构建初始分类器,并从大量未标记样本中在线扩充标记样本,对分类器进行增量式更新以提高其泛化性能.半监督基准数据试验结果表明,训练样本总数相同但标记样本数与未标记样本数比例不同时,所提算法得到的分类准确率相当且训练时间相差小于1.2倍.以柴油机8种工况的故障模式为对象进行试验验证,结果表明标记故障样本较少时,未标记故障样本的加入可使故障分类准确率提高5％～8％.

Fault Diagnosis Method based on Online Semi-supervised Learning

It is difficult to obtain many priori samples during fault diagnosis of machine equipment. However, most of classification methods are not able to catch the latent information from the unlabeled fault samples. So an online semi-supervised learning algorithm is proposed. In the proposed method, online sequential extreme learning machine is extended from supervised learning to semi-supervised learning based on the tri-training algorithm. Three primitive classifiers are builded using the unprecise labeled samples and the classifiers are used to label the unlabeled samples temporarily. Then the samples with high confidence level are choosed to extend the real labeled samples. At the end, the classifiers are also updated via the new labeled samples. Experi- ments on the semi-supervised benchmark data sets show that the proposed algorithm could achieve a small difference on the classification accuracy and difference of the training time less than 1.2 times when the sum of training samples are same but the ratio of labeled samples to unlableled samples is different. The experiment test of fault diagnosis in a diesel is developed. The results show that the online semi-supervised learning algorithm could obtain an increase from 5% to 8% on the classification accuracy when the labeled samples are fewer but the unlabeled samples are added.