access icon openaccess Fault diagnosis using particle filter for MEA typical components

More electric aircraft (MEA) is a developing trend in modern aerospace engineering aiming for a reduction of the aircraft weight, operation cost and environmental impact through putting more emphasis on the utilisation of electrical power. It has many advantages, but also increases the complexity of the aircraft. Therefore, the requirements of prognostic and health management for MEA are needed. The method that using sequential importance re-sampling (SIR) particle filtering state estimation and smoothed residual to diagnose fault for typical components is discussed. The simulation results show that this method can locate faults accurately and quickly.

Inspec keywords: aerospace engineering; aerospace engines; state estimation; aircraft power systems; fault diagnosis; particle filtering (numerical methods)

Other keywords: electric aircraft; SIR particle filtering state estimation; prognostic health management; utilisation; electrical power; environmental impact; modern aerospace engineering; aircraft weight; fault diagnosis; operation cost; particle filter; MEA typical components; developing trend

Subjects: Filtering methods in signal processing; Instrumentation; Engines; Testing; Aerospace industry; Aerospace instrumentation and equipment; Inspection and quality control

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http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.0028
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