title: An Artificial Intelligence Approach for Gears Diagnostics in AUVs.
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.
authors.affiliation: Department of Mechanics, Universidad Nacional de Educación a Distancia (UNED), C/. Juan del Rosal 12, 28040 Madrid, Spain. nicomar@ull.edu.es.|Escuela Politécnica Superior de Ingeniería, Universidad de La Laguna, 38001 Tenerife, Spain. nicomar@ull.edu.es.
authors.forename: Graciliano Nicolás
authors.initials: GN
authors.lastname: Marichal
authors.affiliation: Department of Mechanics, Universidad Nacional de Educación a Distancia (UNED), C/. Juan del Rosal 12, 28040 Madrid, Spain. mlcastillo@ind.uned.es.
authors.forename: María Lourdes
authors.initials: ML
authors.lastname: Del Castillo
authors.affiliation: Department of Mechanical Engineering, Universidad Carlos III de Madrid, Madrid 28911, Spain. jelopez@ing.uc3m.es.
authors.forename: Jesús
authors.initials: J
authors.lastname: López
authors.affiliation: Escuela Politécnica Superior de Ingeniería, Universidad de La Laguna, 38001 Tenerife, Spain. ipadron@ull.es.
authors.forename: Isidro
authors.initials: I
authors.lastname: Padrón
authors.affiliation: Department of Mechanics, Universidad Nacional de Educación a Distancia (UNED), C/. Juan del Rosal 12, 28040 Madrid, Spain. martes@ind.uned.es.
authors.forename: Mariano
authors.initials: M
authors.lastname: Artés
keywords: Condition monitoring; Genetic Neuro-Fuzzy systems; fuzzy logic, AUVs; vibration