Next Article in Journal
MGRA: Motion Gesture Recognition via Accelerometer
Previous Article in Journal
Measurements of Generated Energy/Electrical Quantities from Locomotion Activities Using Piezoelectric Wearable Sensors for Body Motion Energy Harvesting
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 529; doi:10.3390/s16040529

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

1
Department of Mechanics, Universidad Nacional de Educación a Distancia (UNED), C/. Juan del Rosal 12, 28040 Madrid, Spain
2
Escuela Politécnica Superior de Ingeniería, Universidad de La Laguna, 38001 Tenerife, Spain
3
Department of Mechanical Engineering, Universidad Carlos III de Madrid, Madrid 28911, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Xue Wang
Received: 11 February 2016 / Revised: 28 March 2016 / Accepted: 6 April 2016 / Published: 12 April 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1880 KB, uploaded 12 April 2016]   |  

Abstract

In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. View Full-Text
Keywords: Condition monitoring; vibration; Genetic Neuro-Fuzzy systems; fuzzy logic, AUVs Condition monitoring; vibration; Genetic Neuro-Fuzzy systems; fuzzy logic, AUVs
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Marichal, G.N.; Del Castillo, M.L.; López, J.; Padrón, I.; Artés, M. An Artificial Intelligence Approach for Gears Diagnostics in AUVs. Sensors 2016, 16, 529.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top