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

This article presents an effective bearing fault diagnosis model based on multiple extraction and selection techniques. In multiple feature extraction, the discrete wavelet transform, envelope analysis, and fast Fourier transform are considered. While the combined binary particle swarm optimization with extended memory is focusing on feature selection. The current signals are analyzed by discrete wavelet transform. From there, the statistical features in the time and frequency domain are extracted by two techniques: envelope analysis, fast Fourier transform. Subsequently, the binary particle swarm optimization is combined with extended memory and two proposed position update mechanisms to eliminate redundant or irrelevant features to achieve the optimal feature subset. Besides, three classifiers including nai&#x0308;ve Bayes, decision tree, and linear discriminant analysis are applied and compared to select the best model to detect the bearing fault.

Intelligence Bearing Fault Diagnosis Model Using Multiple Feature Extraction and Binary Particle Swarm Optimization With Extended Memory