Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis

Qizhi He, Weiguo Zhang, Peng Lu, Jinglong Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)


This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD.

Original languageEnglish
Article number105649
JournalAerospace Science and Technology
Publication statusPublished - Mar 2020


  • Adaptive two-stage extended Kalman filter
  • Fault detection and diagnosis
  • Inertial measurement unit
  • Iterated optimal two-stage extended Kalman filter
  • Nonlinear disturbance observer
  • Sliding mode observer

ASJC Scopus subject areas

  • Aerospace Engineering

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