Improved model based fault detection technique and application to humanoid robots

Ons Amri, Majdi Mansouri, Ayman Al-Khazraji, Hazem Nounou, Mohamed Nounou, Ahmed Ben Hamida

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

One of the issue of significant interest for robotics is the fault detection, specifically when we have application in risky circumstances. Robotic systems required a capacity to efficiently identify and endure some defects so that they can keep achieving the required tasks while avoiding instantaneous repairing process. Consequently, we aim in this work to propose a systematic approach for state estimation and fault detection technique to enhance the operation of humanoid robots (HR) systems using an extended Kalman filter (EKF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The objectives of this work are sixfold: (1) apply EKF technique to estimate the state variables in HR systems. The EKF is among the most popular nonlinear state estimation methods; (2) use dynamical multiscale representation for obtaining accurate settled characteristics; (3) propose a new optimized EWMA (OEWMA) based on the best selection of both smoothing parameter (λ) and control width L; (4) combine the advantages of state estimation technique with MS-OEWMA chart to improve the monitoring of HR systems; (5) investigate the effect of fault types (change in variance and mean in shift) and fault sizes on the monitoring performances; (6) validate the developed technique using two robot models: inverted pendulum and five-bar linkage. The detection results are evaluated using three fault detection metrics: missed detection rate (MDR), false alarm rate (FAR) and out-of-control average run length (ARL1).

Original languageEnglish
Pages (from-to)140-151
Number of pages12
JournalMechatronics
Volume53
DOIs
Publication statusPublished - 1 Aug 2018

Fingerprint

Fault detection
Extended Kalman filters
State estimation
Robots
Robotics
Monitoring
Pendulums
Defects

Keywords

  • Extended Kalman filter (EKF)
  • Fault detection (FD)
  • Humanoid robot (HR) systems
  • Multiscale representation
  • Optimized EWMA
  • State estimation

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Improved model based fault detection technique and application to humanoid robots. / Amri, Ons; Mansouri, Majdi; Al-Khazraji, Ayman; Nounou, Hazem; Nounou, Mohamed; Ben Hamida, Ahmed.

In: Mechatronics, Vol. 53, 01.08.2018, p. 140-151.

Research output: Contribution to journalArticle

@article{de4fddad8a8a4d5d94208757e5fe392c,
title = "Improved model based fault detection technique and application to humanoid robots",
abstract = "One of the issue of significant interest for robotics is the fault detection, specifically when we have application in risky circumstances. Robotic systems required a capacity to efficiently identify and endure some defects so that they can keep achieving the required tasks while avoiding instantaneous repairing process. Consequently, we aim in this work to propose a systematic approach for state estimation and fault detection technique to enhance the operation of humanoid robots (HR) systems using an extended Kalman filter (EKF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The objectives of this work are sixfold: (1) apply EKF technique to estimate the state variables in HR systems. The EKF is among the most popular nonlinear state estimation methods; (2) use dynamical multiscale representation for obtaining accurate settled characteristics; (3) propose a new optimized EWMA (OEWMA) based on the best selection of both smoothing parameter (λ) and control width L; (4) combine the advantages of state estimation technique with MS-OEWMA chart to improve the monitoring of HR systems; (5) investigate the effect of fault types (change in variance and mean in shift) and fault sizes on the monitoring performances; (6) validate the developed technique using two robot models: inverted pendulum and five-bar linkage. The detection results are evaluated using three fault detection metrics: missed detection rate (MDR), false alarm rate (FAR) and out-of-control average run length (ARL1).",
keywords = "Extended Kalman filter (EKF), Fault detection (FD), Humanoid robot (HR) systems, Multiscale representation, Optimized EWMA, State estimation",
author = "Ons Amri and Majdi Mansouri and Ayman Al-Khazraji and Hazem Nounou and Mohamed Nounou and {Ben Hamida}, Ahmed",
year = "2018",
month = "8",
day = "1",
doi = "10.1016/j.mechatronics.2018.06.006",
language = "English",
volume = "53",
pages = "140--151",
journal = "Mechatronics",
issn = "0957-4158",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Improved model based fault detection technique and application to humanoid robots

AU - Amri, Ons

AU - Mansouri, Majdi

AU - Al-Khazraji, Ayman

AU - Nounou, Hazem

AU - Nounou, Mohamed

AU - Ben Hamida, Ahmed

PY - 2018/8/1

Y1 - 2018/8/1

N2 - One of the issue of significant interest for robotics is the fault detection, specifically when we have application in risky circumstances. Robotic systems required a capacity to efficiently identify and endure some defects so that they can keep achieving the required tasks while avoiding instantaneous repairing process. Consequently, we aim in this work to propose a systematic approach for state estimation and fault detection technique to enhance the operation of humanoid robots (HR) systems using an extended Kalman filter (EKF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The objectives of this work are sixfold: (1) apply EKF technique to estimate the state variables in HR systems. The EKF is among the most popular nonlinear state estimation methods; (2) use dynamical multiscale representation for obtaining accurate settled characteristics; (3) propose a new optimized EWMA (OEWMA) based on the best selection of both smoothing parameter (λ) and control width L; (4) combine the advantages of state estimation technique with MS-OEWMA chart to improve the monitoring of HR systems; (5) investigate the effect of fault types (change in variance and mean in shift) and fault sizes on the monitoring performances; (6) validate the developed technique using two robot models: inverted pendulum and five-bar linkage. The detection results are evaluated using three fault detection metrics: missed detection rate (MDR), false alarm rate (FAR) and out-of-control average run length (ARL1).

AB - One of the issue of significant interest for robotics is the fault detection, specifically when we have application in risky circumstances. Robotic systems required a capacity to efficiently identify and endure some defects so that they can keep achieving the required tasks while avoiding instantaneous repairing process. Consequently, we aim in this work to propose a systematic approach for state estimation and fault detection technique to enhance the operation of humanoid robots (HR) systems using an extended Kalman filter (EKF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The objectives of this work are sixfold: (1) apply EKF technique to estimate the state variables in HR systems. The EKF is among the most popular nonlinear state estimation methods; (2) use dynamical multiscale representation for obtaining accurate settled characteristics; (3) propose a new optimized EWMA (OEWMA) based on the best selection of both smoothing parameter (λ) and control width L; (4) combine the advantages of state estimation technique with MS-OEWMA chart to improve the monitoring of HR systems; (5) investigate the effect of fault types (change in variance and mean in shift) and fault sizes on the monitoring performances; (6) validate the developed technique using two robot models: inverted pendulum and five-bar linkage. The detection results are evaluated using three fault detection metrics: missed detection rate (MDR), false alarm rate (FAR) and out-of-control average run length (ARL1).

KW - Extended Kalman filter (EKF)

KW - Fault detection (FD)

KW - Humanoid robot (HR) systems

KW - Multiscale representation

KW - Optimized EWMA

KW - State estimation

UR - http://www.scopus.com/inward/record.url?scp=85048827934&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048827934&partnerID=8YFLogxK

U2 - 10.1016/j.mechatronics.2018.06.006

DO - 10.1016/j.mechatronics.2018.06.006

M3 - Article

VL - 53

SP - 140

EP - 151

JO - Mechatronics

JF - Mechatronics

SN - 0957-4158

ER -