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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

International audienceCascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applicationsthanks to its many advantages. However, the reliability of a CHMI is decreased with the increaseof its levels. Fault diagnosis techniques play a key role in ensuring the reliability of a CHMI.The performance of a fault diagnosis method depends on the characteristics of the extracted features.In practice, some extracted features may be very similar to ensure a good diagnosis performance atsome H-bridges of CHMI. The situation becomes even worse in the presence of noise. To fix theseproblems, in this paper, signal denoising and data preprocessing techniques are firstly developed.Then, a Principal Components Rearrangement method (PCR) is proposed to represent the differentfeatures sufficiently distinct from each other. Finally, a PCR-based fault diagnosis strategy is designed.The performance of the proposed strategy is compared with other fault diagnosis strategies, based ona 7-level CHMI hardware platform

A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI