Health estimation of servo actuator using linearized predictive time domain method to check developing fault for small unmanned aerial vehicle

Zulhilmy Sahwee, Nazaruddin Abd Rahman, Khairul Salleh Mohamed Sahari, Aina Suriani Mahmood

Research output: Contribution to journalArticle

Abstract

The degrading performance of actuators in small unmanned aerial Vehicle (UAV) is often left unnoticed because it is masked by autopilot control. The faulty actuator will only be detected when the actuator has been severely damaged. If it occurs during flight, the UAV will be lost, same goes with the valuable data on the board. Usually, a pre-flight check is performed before each flight to ensure the overall condition of the UAV including the actuators. The actuator health deterioration is difficult to be recognized by visual inspection. This paper presents a method to detect the health of the actuator through the integration of Built-in Test System (BITE) using offline model estimation method. Least square regression estimation was performed on the healthy actuator for training data using fixed and increasing input signal. The fault is then simulated to test the training data accuracy in detecting actuator fault. An analysis is performed to show the advantage and disadvantage of each technique used. The estimation technique described was able to detect faulty actuator which could then be integrated with on-board health detection system in order to increase the reliability of the UAV.

Original languageEnglish
Pages (from-to)5029-5033
Number of pages5
JournalAdvanced Science Letters
Volume23
Issue number6
DOIs
Publication statusPublished - 01 Jan 2017

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Unmanned aerial vehicles (UAV)
flight
Time Domain
Actuator
Health
Fault
Actuators
health
estimation procedure
Least-Squares Analysis
estimation method
regression
performance
Autopilot
time
method
vehicle
Regression Estimation
Least Squares Regression
Least Squares Estimation

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Health(social science)
  • Mathematics(all)
  • Education
  • Environmental Science(all)
  • Engineering(all)
  • Energy(all)

Cite this

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abstract = "The degrading performance of actuators in small unmanned aerial Vehicle (UAV) is often left unnoticed because it is masked by autopilot control. The faulty actuator will only be detected when the actuator has been severely damaged. If it occurs during flight, the UAV will be lost, same goes with the valuable data on the board. Usually, a pre-flight check is performed before each flight to ensure the overall condition of the UAV including the actuators. The actuator health deterioration is difficult to be recognized by visual inspection. This paper presents a method to detect the health of the actuator through the integration of Built-in Test System (BITE) using offline model estimation method. Least square regression estimation was performed on the healthy actuator for training data using fixed and increasing input signal. The fault is then simulated to test the training data accuracy in detecting actuator fault. An analysis is performed to show the advantage and disadvantage of each technique used. The estimation technique described was able to detect faulty actuator which could then be integrated with on-board health detection system in order to increase the reliability of the UAV.",
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AU - Mahmood, Aina Suriani

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AB - The degrading performance of actuators in small unmanned aerial Vehicle (UAV) is often left unnoticed because it is masked by autopilot control. The faulty actuator will only be detected when the actuator has been severely damaged. If it occurs during flight, the UAV will be lost, same goes with the valuable data on the board. Usually, a pre-flight check is performed before each flight to ensure the overall condition of the UAV including the actuators. The actuator health deterioration is difficult to be recognized by visual inspection. This paper presents a method to detect the health of the actuator through the integration of Built-in Test System (BITE) using offline model estimation method. Least square regression estimation was performed on the healthy actuator for training data using fixed and increasing input signal. The fault is then simulated to test the training data accuracy in detecting actuator fault. An analysis is performed to show the advantage and disadvantage of each technique used. The estimation technique described was able to detect faulty actuator which could then be integrated with on-board health detection system in order to increase the reliability of the UAV.

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