Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
An approach to fault diagnosis of rolling bearings
WSEAS Transactions on Systems and Control
EE'10 Proceedings of the 5th IASME/WSEAS international conference on Energy & environment
Wind maintenance system using network synchronization techniques based on open-source software
EE'10 Proceedings of the 5th IASME/WSEAS international conference on Energy & environment
A better environment through better terology
EE'10 Proceedings of the 5th IASME/WSEAS international conference on Energy & environment
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Wind generators maintenance can be performed in a systematic way because they are built with equipments with known reliability and maintenance parameters known that can be used in the planned maintenance. However, it is possible to increase the reliability of the system so as to rationalize the costs, by developing new management models and new algorithms based on on-line monitoring of several parameters, namely vibrations, electrical variables, among others. It is because of this kind of challenge that the authors are developing new methodologies in the area of wind generators that, as we will demonstrate, can contribute to a better environment. The methodology proposed aims to optimize the cycles of production and, consequently, reduce other kinds of energy production. The new methodologies will be, later, incorporated through new predictive maintenance modules in an integrated maintenance management system called SMIT (Terology Integrated Modular System). The base of SMIT is accessed through a client-server system and a browser system that includes the main modules of a traditional system, as well as a fault diagnosis module, a nonperiodic maintenance planning module and a generic on-condition maintenance module, among other innovations. The new features will include, in the case of wind generators, on-line measures and the corresponding on-time treatment, using algorithms based on time-series forecasting.