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/

Zendy Plus

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

Zendy Tools

Zendy Open

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine