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
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Maintenance management is a subject that, instead of reducing importance, with the increase of equipment reliability, it increases its role in the companies and obliges the increase of the level of demand of professionals involved because of the new technical and environmental demands. Sometimes, scientific developments anticipate the company's needs while other times it is the company that challenges science. The maintenance area is an example that offers challenges to both science and companies in order to optimize the performance of equipment and facilities. This is also the case of wind generators, because their expansion, evolution, maintenance and reliability guarantee, needs to be adequately articulated in order to maximize production time and, obviously, to optimize maintenance interventions. It is because of this kind of challenge that the authors are developing new methodologies in the area of wind generators that aims to optimize the cycles of production and, consequently, reduce other kinds of energy production. The new features include on-line measures and the corresponding on-time treatment, using algorithms based on time-series forecasting and wireless technology to transmit the signals. The prediction models uses regression techniques based on SVR, ARMA and ARIMA models, modified according to this specific case. The weather conditions and the technical and construction characteristics of wind generators are only some variables that we have in account in the models that are under development. But, if these conditions are important, it is also very important to collect, read and treat data from sensors placed in wind generators that, because their geographic dispersion, and difficulty of transmission, must be solved adequately and conjugated with the above referred algorithms, in order to implement an adequate system. This is the ambit of the present article that reports a wide approach of a subject that usually is managed separately, this is, the hardware from one side and the prediction algorithms from other side. This is possible because the team has being researching and developing algorithms and an information system, since many years ago, around the terology subject that is a wider vision of maintenance. Then, the new methodologies, above mentioned, 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 non-periodic maintenance planning module and a generic on-condition maintenance module, among other innovations.