A computer system for predictive maintenance of wind generators

• Type: Article in International Conference Proceedings Book
• Date: 2008

Authors

Fonseca, I (Author) Other; Farinha, T (Author) Other; Fernando Maciel Barbosa (Author) FEUP

Conference proceedings International

Title: PROCEEDINGS OF THE 12TH WSEAS INTERNATIONAL CONFERENCE ON COMPUTERS , PTS 1-3: NEW ASPECTS OF COMPUTERS

Pages: 928-933

12th WSEAS International Conference on Computers

Heraklion, GREECE, JUL 23-25, 2008

Indexing

ISI Web of Knowledge - 6 Citations

Scientific classification

FOS: Engineering and technology > Other engineering and technologies

Other information

Authenticus ID: P-004-4GV

Abstract (EN): 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 non-periodic 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6