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

Inspired by recent progress in machine learning, a data-driven faultdiagnosis and isolation (FDI) scheme is explicitly developed for failure in thefuel supply system and sensor measurements of the laboratory gas turbinesystem. A passive approach of fault diagnosis is implemented where a model istrained using machine learning classifiers to detect a given set of faultscenarios in real-time on which it is trained. Towards the end, a comparativestudy is presented for well-known classification techniques, namely Supportvector classifier, linear discriminant analysis, K-neighbor, and decisiontrees. Several simulation studies were carried out to demonstrate andillustrate the proposed fault diagnosis scheme's advantages, capabilities, andperformance.

On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine