Nowadays, the measurement technology of multichannel information fusion provides a solid research foundation for digital and intelligent fault diagnosis of mechatronics equipment. To implement the rapid fusion of multichannel data and intelligent diagnosis, a new fault diagnosis method for multichannel motor–rotor system via multimanifold deep extreme learning machine (MDELM) algorithm is first proposed in this article. Specifically, the designed MDELM algorithm is divided into two main components: 1) unsupervised self-taught feature extraction via the designed extreme learning machine based-modified sparse filtering feature extractor; 2) semisupervised fault classification via the designed MELM classifier with multimanifold constraints to mine the intraclass and interclass discriminant feature information. Experimental and industrial data from motor–rotor system demonstrates the superiority of the proposed method and algorithms. Compared with other fault diagnosis methods, the proposed MDELM algorithm has better learning efficiency, and it is more suitable for intelligent diagnosis of multichannel data fusion.

中文翻译：

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基于多流形深度极限学习机的多通道电机转子系统智能故障诊断

如今，多通道信息融合的测量技术为机电设备的数字化和智能化故障诊断提供了坚实的研究基础。为了实现多通道数据和智能诊断的快速融合，本文首次提出了一种通过多流形深度极限学习机（MDELM）算法对多通道电机－转子系统进行故障诊断的新方法。具体而言，设计的MDELM算法分为两个主要部分：1）通过设计的基于极限学习机的改进型稀疏滤波特征提取器进行无监督的自学特征提取；2）通过设计的具有多流形约束的MELM分类器进行半监督故障分类，以挖掘类内和类间判别特征信息。来自电动机-转子系统的实验和工业数据证明了所提出的方法和算法的优越性。与其他故障诊断方法相比，本文提出的MDELM算法具有更高的学习效率，更适合于多通道数据融合的智能诊断。