Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle

Industries are moving towards automation and the usage of machines such as Automated Guided Vehicle (AGV) is increasing. Thus, demands for the reliability of AGVs are increasing as they have various complex tasks to carry out. Unfortunately, AGVs are still susceptible to faults and breakdown. Theref...

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Main Author:	Dares, Marvin
Format:	Thesis
Language:	English
Published:	2023
Subjects:	TJ Mechanical engineering and machinery TP Chemical technology
Online Access:	http://eprints.utm.my/102527/1/MarvinDaresMSChE2023.pdf
Tags:	Add Tag agv 1

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record_format	uketd_dc
spelling	my-utm-ep.1025272023-09-09T01:15:04Z Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle 2023 Dares, Marvin TJ Mechanical engineering and machinery TP Chemical technology Industries are moving towards automation and the usage of machines such as Automated Guided Vehicle (AGV) is increasing. Thus, demands for the reliability of AGVs are increasing as they have various complex tasks to carry out. Unfortunately, AGVs are still susceptible to faults and breakdown. Therefore, fault detection is important to provide means of self-diagnosis on AGV. However, fault detections are generally threshold based which are unsatisfying in terms of accuracy and are prone to false triggering. Extended Kalman Filter (EKF) has limitations in handling nonlinear models while Unscented Kalman Filter (UKF) seems promising. Support Vector Machine (SVM) was used as a fault detection method. Thus, this research proposes a sensor fusion enhanced with SVM for fault detection on AGV. The first objective of this research is to develop a test AGV. This AGV is a two-wheeled differential driven mobile robot with multiple sensors and able to make various types of movements to emulate an industrial AGV. Next objective is to develop an enhanced sensor fusion method using EKF and UKF for fault detection with SVM on AGV. The last objective is to evaluate the performance of the developed method. Experiments were carried out where the AGV was used as a test bed for sensor fusion and fault detection. The AGV was tested in different experiment setups such as different track layout, different wheel condition, and different castor conditions. Result shows that UKF handles changes and non-linearity better than EKF. The average residual generated during the test for UKF is 0.0083 meter while for EKF is 0.0129 meter. With sensor fusion, deviations in odometry data can be compensated with the usage of a LiDAR sensor as reference. Using UKF parameters to detect fault, the accuracy achieved with SVM is 64.2% compared to 37.9% without SVM. Fault detection accuracy using EKF parameters with SVM is 82.5% while without SVM is 41.0%. As a conclusion, the results show SVM improves fault detection accuracy regardless of using UKF or EKF. 2023 Thesis http://eprints.utm.my/102527/ http://eprints.utm.my/102527/1/MarvinDaresMSChE2023.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:152389 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Chemical & Energy Engineering Faculty of Engineering - School of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TJ Mechanical engineering and machinery TP Chemical technology
spellingShingle	TJ Mechanical engineering and machinery TP Chemical technology Dares, Marvin Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
description	Industries are moving towards automation and the usage of machines such as Automated Guided Vehicle (AGV) is increasing. Thus, demands for the reliability of AGVs are increasing as they have various complex tasks to carry out. Unfortunately, AGVs are still susceptible to faults and breakdown. Therefore, fault detection is important to provide means of self-diagnosis on AGV. However, fault detections are generally threshold based which are unsatisfying in terms of accuracy and are prone to false triggering. Extended Kalman Filter (EKF) has limitations in handling nonlinear models while Unscented Kalman Filter (UKF) seems promising. Support Vector Machine (SVM) was used as a fault detection method. Thus, this research proposes a sensor fusion enhanced with SVM for fault detection on AGV. The first objective of this research is to develop a test AGV. This AGV is a two-wheeled differential driven mobile robot with multiple sensors and able to make various types of movements to emulate an industrial AGV. Next objective is to develop an enhanced sensor fusion method using EKF and UKF for fault detection with SVM on AGV. The last objective is to evaluate the performance of the developed method. Experiments were carried out where the AGV was used as a test bed for sensor fusion and fault detection. The AGV was tested in different experiment setups such as different track layout, different wheel condition, and different castor conditions. Result shows that UKF handles changes and non-linearity better than EKF. The average residual generated during the test for UKF is 0.0083 meter while for EKF is 0.0129 meter. With sensor fusion, deviations in odometry data can be compensated with the usage of a LiDAR sensor as reference. Using UKF parameters to detect fault, the accuracy achieved with SVM is 64.2% compared to 37.9% without SVM. Fault detection accuracy using EKF parameters with SVM is 82.5% while without SVM is 41.0%. As a conclusion, the results show SVM improves fault detection accuracy regardless of using UKF or EKF.
format	Thesis
qualification_level	Master's degree
author	Dares, Marvin
author_facet	Dares, Marvin
author_sort	Dares, Marvin
title	Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
title_short	Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
title_full	Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
title_fullStr	Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
title_full_unstemmed	Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle
title_sort	sensor fusion with kalman filter and support vector machine for fault detection in automated guided vehicle
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Chemical & Energy Engineering
granting_department	Faculty of Engineering - School of Chemical & Energy Engineering
publishDate	2023
url	http://eprints.utm.my/102527/1/MarvinDaresMSChE2023.pdf
_version_	1783729173067464704

Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle

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