دانلود مقاله ISI انگلیسی شماره 21822
عنوان فارسی مقاله

SIMAP: سیستم هوشمند برای نگهداری پیشگویانه: برنامه ای برای نظارت بر وضعیت سلامت گیربکس توربین بادی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
21822 2006 17 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers in Industry, Volume 57, Issue 6, August 2006, Pages 552–568

کلمات کلیدی
- نگهداری پیشگویانه - اثر تعمیر و نگهداری - وضعیت بهداشت و درمان - تشخیص - هوش مصنوعی -
پیش نمایش مقاله
پیش نمایش مقاله SIMAP: سیستم هوشمند برای نگهداری پیشگویانه: برنامه ای برای نظارت بر وضعیت سلامت گیربکس توربین بادی

چکیده انگلیسی

SIMAP is the abbreviated name for the Intelligent System for Predictive Maintenance. It is a software application addressed to the diagnosis in real-time of industrial processes. It takes into account the information coming in real-time from different sensors and other information sources and tries to detect possible anomalies in the normal behaviour expected of the industrial components. The incipient detection of anomalies allows for an early diagnosis and the possibility to plan effective maintenance actions. Also, the continuous monitoring performed allows for an estimation in a qualitative form of the health condition of the components. SIMAP is a general tool oriented to the diagnosis and maintenance of industrial processes, however the first experience of its application has been at a windfarm. In this real case, SIMAP is able to optimize and to dynamically adapt a maintenance calendar for a monitored windturbine according to the real needs and operating life of it as well as other technical and economical criteria. In particular this paper presents the application of SIMAP to the health condition monitoring of a windturbine gearbox as an example of its capabilities and main features.

مقدمه انگلیسی

The use of wind is one of the most attractive new sources of energy at the present moment, as can be seen by the growing installation of windfarms all over the world. Windturbines are relatively young machines where the application of a correct maintenance strategy would be very important for the protection of their future life, productivity and profitability [1]. The current practice of maintenance applied to the existing aerogenerators is based on periodical or preventive maintenance actions recommended by their manufacturers. These are good and general guidelines for the maintenance of aerogenerators, however they do not focus on the specific characteristics of the real and local life of them such as: weather conditions at the location, stress by over-load, hours continuously working, etc. These factors determine the particular life and health of each aerogenerator and for this reason the maintenance applied has to also take them into account. In order to do this, a predictive maintenance plan is the best option to guarantee the long life of the new investments in aerogenerators due to the maintenance actions which are applied according to the real and specific health conditions of every aerogenerator during its life and not only based on general guidelines. When thinking about a predictive maintenance strategy for aerogenerators, it is important to remark that windturbines are quite new machines using an important number of sensors able to supply information to different controllers in order to perform the best control and efficient operation of them. The information collected by the sensors of aerogenerators for control purposes can also be used for monitoring the health condition of their different components and to apply a predictive maintenance plan. According to this, no new investment in sensors is required in order to perform an effective windturbine predictive maintenance strategy because all the aerogenerators include a set of sensors from the manufacturer for different aspects of the control of their elements. The information from these sensors can also be used as main information source for a predictive maintenance plan. This paper presents the architecture of a new predictive maintenance system, called SIMAP, based on artificial intelligent techniques. Its predictive maintenance strategy can be applied to any industrial system or equipment and its main goal is to find the most appropriate time to carry out the needed maintenance actions both from a component health condition and an incipient failure diagnosis perspectives. The new and positive aspects of this predictive maintenance methodology have been tested in windturbines. SIMAP is able to create and dynamically adapt a maintenance calendar for the windturbine that it is monitoring. The criteria followed are set up according to the real needs and operating life of the windturbine. This process is performed on-line and is different from the traditional scheduled maintenance plan based on fixed time intervals following the manufacturer criteria which do not focus on the real operation conditions of the aerogenerators. According to this, SIMAP implements the main aspects of an e-maintenance approach in a computer network such as local and remote continuous monitoring and diagnosis of the main components of the aerogenerators, maintenance actions planned according to the current and historical information collected, distribution of the on-line diagnosis and maintenance workload in different modules interconnected through a computer network and finally, different levels of warnings for the operator. This predictive maintenance system has been developed and applied to a windfarm owned by a Spanish wind energy company called Molinos del Ebro, S.A. This paper provides in Section 2 an overview of the main features and architecture of SIMAP and, in Sections 3, 4, 5, 6, 7, 8, 8.1, 8.2, 8.3, 8.4 and 9, presents the capabilities of SIMAP applied to a particular case of a windturbine, which is the possible failures in a gearbox and how SIMAP works in real-time to detect and diagnose anomalies in this component, to evaluate its health condition and to plan predictive maintenance actions.

نتیجه گیری انگلیسی

The main new features of SIMAP in the field of diagnosis and maintenance introduced in this paper have been the following: - the integration and cooperation of every task involved in a formal predictive maintenance strategy, that is, mainly: continuous monitoring, incipient failure detection and diagnosis, health condition evaluation, predictive maintenance scheduling and effectiveness measure of maintenance actions performed; - on-line and automatic components health condition evaluation, based on a degradation perspective; - a maintenance scheduling method which considers both technical and economical criteria; - on-line, direct and automatic measurement of applied maintenance actions effectiveness, by means of the change observed in the health condition and degradation of the components affected by these maintenance actions. Furthermore, this study concludes that artificial intelligence and modelling techniques are adequate for reaching the main goals of this predictive maintenance strategy, due mainly to their ability to: - model dynamic non-linear industrial processes, by means of artificial neural networks; - characterize and represent both quantitative knowledge coming from historical data (by means of artificial neural networks) as well as qualitative knowledge coming from maintenance and operation experts (by means of expert systems); - perform a dynamical multi-objective non-linear optimization with constraints, by means of genetic algorithms; - represent the uncertainty inherent to the knowledge issued, by means of fuzzy logic. An example of how SIMAP works has been presented, focusing on the on-line health condition monitoring of a windturbine gearbox. Future works are oriented to the monitoring and experience derived from the new maintenance plan implemented in different windfarms.

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