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Robotics
Special Issues
Fault Diagnosis and Fault-Tolerant Control in Robotic Systems
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Fault Diagnosis and Fault-Tolerant Control in Robotic Systems

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 12221

Special Issue Editors

Prof. Dr. Sunan Huang

E-Mail Website
Guest Editor
Temasek Laboratories, National University of Singapore, Singapore
Interests: fault diagnosis and fault-tolerant control; adaptive control; collision avoidance and control of multi-UAVs
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jing Wang

E-Mail Website
Guest Editor
School of Electrical and Control Engineering, North China University of Technology, Beijing, China
Interests: fault diagnosis and state monitoring; fault-tolerant control; iterative learning control; biochemical process synthesis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andy Tyrrell

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of York, Heslington, York YO10 5DD, UK
Interests: bio-inspired computing and AI; evolutionary algorithms; evolutionary computing; fault-tolerant design; microprocessor design; reconfigurable systems; robotics

Special Issue Information

Dear Colleagues,

Fault diagnosis and fault-tolerant control play an important role in robotic systems. This field does not only concern the industries but is also active in the academic communities, involving many disciplines, such as actuator, sensing technology, computer control, image processing, and signal processing technology. It mainly relies on measurement and online analysis based on collected data. The purpose of this Special Issue is to publish the most recent research results and industrial applications in fault diagnosis and fault-tolerant control methods.

We invite academic and industry researchers to submit original manuscripts to this Special Issue that develop research works related to this field. Contributions are invited from various sections of robotic systems where theory and methodology have been developed within the scope of fault diagnosis and fault-tolerant control. Academic research papers are also welcome if they have some new theoretical results on this field, even they are not in robotic systems.

Prof. Dr. Sunan Huang
Prof. Dr. Jing Wang
Prof. Dr. Andy Tyrrell
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fault diagnosis
  • Fault detection
  • Fault isolation
  • Fault identification
  • Fault tolerant control
  • Diagnostic algorithms
  • Intelligent fault diagnosis and fault-tolerant control
  • System monitoring
  • Data-driven fault diagnosis systems
  • Fault diagnosis and fault-tolerant control of multi-agent systems
  • Risk estimation of control systems
  • Prognostic and health management

Published Papers (1 paper)

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Research

17 pages, 2714 KiB  
Article
Fault Diagnosis for UAV Blades Using Artificial Neural Network
by Gino Iannace, Giuseppe Ciaburro and Amelia Trematerra
Robotics 2019, 8(3), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/robotics8030059 - 20 Jul 2019
Cited by 97 | Viewed by 11313
Abstract
In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of [...] Read more.
In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control in Robotic Systems)
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