Improved model based fault detection technique and application to humanoid robots
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© 2018 Elsevier Ltd 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).
author list (cited authors)
Amri, O., Mansouri, M., Al-Khazraji, A., Nounou, H., Nounou, M., & Hamida, A. B.