Abstract:

It is difficult to obtain labeled fault data while there are a multitude of unlabeled data available in industrial process, so how to utilize data information effectively is an important focal point in the field of fault diagnosis. A new semi-supervised learning method, reconstruction-based semi-supervised extreme learning machine (RSELM), was proposed for more sufficient data mining and information usage. Compared to traditional semi-supervised ELM, RSELM replaced random input weight in hidden layer with output weight, which was obtained by ELM auto-encoder (ELM-AE), such that data feature was extracterd more effectively. Since data could be reconstructed linearly by its neighbors, a self-adaptive reconstruction graph of neighboring data in combination with connection weight of optimal labeled data better reflected data structure information. A novel objective function preserving local structure information was further built to train classifier effectively. Simulation experiment on standard datasets and TE process demonstrated effectiveness of the proposed algorithm.

Key words: semi-supervised ELM, reconstruction, ELM-AE, fault diagnosis

摘要：

工业过程中获取带标签的故障数据困难，而无标签故障数据却大量存在，如何有效地利用数据信息进行故障诊断是故障诊断领域的重要内容。为更充分地挖掘和利用数据信息，提出一种新的半监督学习方法：基于重构的半监督极限学习机（RSELM）。相比于传统的半监督极限学习机（ELM）方法，RSELM采用自动编码ELM（ELM-AE）获得的输出权重替代随机的隐含层输入权重，能更有效地提取数据特征；考虑到数据均可由其近邻数据来线性重构，故可构建近邻数自适应选择的重构图，并同时利用数据的标签信息优化连接权重，以更优地反映数据结构信息；通过建立新的含局部保持的目标函数，可有效地训练分类器。标准数据集和TE过程上的仿真实验验证了所提算法的有效性。

关键词: 半监督极限学习机, 重构, ELM-AE, 故障诊断