دانلود مقاله ISI انگلیسی شماره 46362
عنوان فارسی مقاله

تعمیر و نگهداری تسمه نقاله با استفاده از یک سیستم خبره مبتنی بر منطق فازی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46362 2015 7 صفحه PDF سفارش دهید 4200 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Maintenance of belt conveyors using an expert system based on fuzzy logic
منبع

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

Journal : Archives of Civil and Mechanical Engineering, Volume 15, Issue 2, February 2015, Pages 412–418

کلمات کلیدی
سیستم های مدیریت تعمیر و نگهداری به کمک کامپیوتر - تسمه نقاله - منطق فازی
پیش نمایش مقاله
پیش نمایش مقاله تعمیر و نگهداری تسمه نقاله با استفاده از یک سیستم خبره مبتنی بر منطق فازی

چکیده انگلیسی

In recent years, conveyor belt transport systems have taken on a new significance due to numerous research studies on innovative design solutions. The application of these new developed solutions leads to considerable reduction in operational costs of transport systems, while ensuring their high reliability and service life at the same time. Nonetheless, there are still areas that pose challenge to both research and development. Typical challenges are analyzed in this paper. The solution to the problems of conveyor transport maintenance can be the implementation of a system for estimation of technical condition of conveyor belt joints. It serves as a second level safety diagnostic system for transport. Besides real-time measurements, the system enables a long-term analysis of historic data for every single joint that makes up the conveyor belt loop, from the moment of its manufacture to the final operation. The effectiveness of a conveyor belt diagnostic system primarily depends on the use of a decision supporting system. With adequate inference rules applied, this system would increase the effectiveness and shorten the time of decision-making as well as verify generated signals. The above tasks can be performed by a suitable expert system that predicts values of the analyzed time series, using the predicted values and inference rules to verify any potential false alarm signals at the same time. The idea and algorithm of such an expert system were presented in this article as well.

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