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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Our Work come in forward of several investigations in motor diagnosis field combining the use of Motor current demodulation analysis (MCDA) and the multilayer feed-forward artificial neural networks (ANNs) optimized by genetic algorithms (GAs). ANNs are used effectively to recognize Broken Rotor bar Faults severity. The novelty in this work is that proposed methodology is based on the extraction of fault components from the spectrum of both current amplitude and phase modulation (AM and PM), AM and PM signals are extracted by signal demodulation techniques from the measured stator current ; this methodology have provided more accurate results on faulty tested motors at different loads.

Motor Current Demodulation Analysis applied with Neural Networks and Genetic Algorithms for Rotor Bar Faults Diagnosis