کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7563006 1491532 2015 39 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Switching LDS-based approach for process fault detection and classification
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Switching LDS-based approach for process fault detection and classification
چکیده انگلیسی
Dynamic and uncertainty are two main features of the industrial processes data which should be paid attentions when carrying out process monitoring and fault diagnosis. As a typical dynamic Bayesian network model, linear dynamic system (LDS) can efficiently deal with both dynamic and uncertain features of the process data. This paper proposes a switching form of the LDS model for fault detection and classification. A novel and convenient learning algorithm is developed for parameter estimation of the switching LDS model, and the Gaussian Sum Filtering method is introduced for online fault classification. Besides, a switching LDS based threshold statistic is defined for unknown fault detection. Detailed comparative studies are carried out on the Tennessee Eastman (TE) benchmark process among Fisher Discriminant Analysis (FDA), Support Vector Machines (SVM), Hidden Markov Model (HMM), and the proposed method. Simulation results show the superiority of switching LDS over other three methods in terms of fault classification. Furthermore, switching LDS model provides an additional online fault classification mechanism. Simulation results in the same process demonstrate that switching LDS approach can achieve accurate detection rate of the unknown fault with minimal false alarms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 146, 15 August 2015, Pages 169-178
نویسندگان
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