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Sensors 2016, 16(4), 529;

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

Department of Mechanics, Universidad Nacional de Educación a Distancia (UNED), C/. Juan del Rosal 12, 28040 Madrid, Spain
Escuela Politécnica Superior de Ingeniería, Universidad de La Laguna, 38001 Tenerife, Spain
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)
Full-Text   |   PDF [1880 KB, uploaded 12 April 2016]   |  


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

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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.

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