Abstract
Investigators publish new report on Machine Learning - Intelligent Systems. According to news reporting originating from Qingdao, People’s Republic of China, by NewsRx correspondents, research stated, “The intelligent fault diagnosis model has made a significant development, whose highprecision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data.” Financial support for this research came from Science and Technology Innovation 2025 Major Project of Ningbo. Our news editors obtained a quote from the research from the China University of Petroleum, “It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem.”
NETL
NSTL
Springer Nature