کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380575 1437444 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine health condition prediction via online dynamic fuzzy neural networks
ترجمه فارسی عنوان
پیش بینی وضعیت سلامت دستگاه از طریق شبکه های عصبی فازی پویا آنلاین
کلمات کلیدی
شبکه عصبی فازی وضعیت سلامتی دستگاه، سری زمانی غیر ثابت پیش آگهی چند مرحله ای یادگیری در زمان واقعی، سازماندهی خود
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Machine health condition (MHC) prediction is useful for preventing unexpected failures and minimizing overall maintenance costs in condition-based maintenance. The neural network (NN)-based data-driven method has been considered to be promising for MHC prediction due to the adaptability, nonlinearity and universal approximation capability of NNs. This paper presents an online MHC prediction approach using online dynamic fuzzy NNs (OD-FNNs) with structure and parameters learning. To meet the requirement of real-time application, the original OD-FNN is simplified based on an extreme learning machine technique as follows: (1) initial fuzzy rules are randomly generated without the knowledge of training data; (2) fuzzy rules are added and pruned uniformly by fired strength-based criteria; (3) antecedent parameters are fixed after generation so that only consequent parameters are updated online. The modified OD-FNN is particularly suitable for MHC prediction since: (1) fuzzy rules can evolve as new training datum arrives, which enables us to cope with non-stationary processes in MHC; (2) learning mechanisms applied are simple and efficient for real-time implementation. The validity and superiority of the proposed MHC prediction approach has been evaluated by real-world monitoring data from the accelerated bearing life.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 35, October 2014, Pages 105–113
نویسندگان
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