کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6594707 1423729 2018 38 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fault detection and diagnosis using empirical mode decomposition based principal component analysis
ترجمه فارسی عنوان
تشخیص و تشخیص گسل با استفاده از تجزیه و تحلیل مولفه های مبتنی بر تجزیه حالت تجربی
کلمات کلیدی
نظارت و کنترل فرایند، گسل های تصادفی، تجزیه و تحلیل عدم قطعیت، مهندسی سیستم، تجزیه و تحلیل داده پردازش،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
چکیده انگلیسی
This paper presents a new algorithm to identify and diagnose stochastic faults in Tennessee Eastman (TE) process. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) to diagnose a group of faults that could not be properly detected and/or diagnosed with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, fault detection, and fault diagnosis. Measured variables are first decomposed into different scales using the EEMD-based PCA, from which fault signatures can be extracted for fault detection and diagnosis (FDD). The T2 and Q statistics-based CUSUMs are further applied to improve fault detection, where a set of PCA models are developed from historical data to characterize anomalous fingerprints that are correlated with each fault for accurate fault diagnosis. The algorithm developed in this paper can successfully identify and diagnose both individual and simultaneous occurrences of stochastic faults.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 115, 12 July 2018, Pages 1-21
نویسندگان
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