English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Intelligent diagnosis method has become a new focus for researchers, which can get rid of the dependence of diagnostic experience and prior knowledge. However, in practical application, to deal with the new fault type of mechanical equipment, the number of fault labels of the diagnosis model needs to be increased. We must retrain the whole training model, which is a time-consuming process. To solve this problem, higher requirements are put forward for the generalization ability and universality of the algorithm. In view of the feature extraction advantages of cross-sparse filtering (Cr-SF), which can be regarded as an unsupervised minimum entropy learning method using the maximization of the proxy of sparsity, this paper proposed a parallel network based on Cr-SF. The feature extraction process of each sample is independent, and the feature extraction and classifier training process are separated. Therefore, the most prominent advantage of the proposed method is that when a new fault occurs, it only needs to extract the feature of the new fault separately and then input it to the classifier at the last layer for training. The experimental results show that the proposed method can obtain high accuracy and stability and can significantly improve the adaptability of intelligent fault diagnosis in practical application.

Parallel Cross-Sparse Filtering Networks and Its Application on Fault Diagnosis of Rotating Machinery