کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7104454 | 1460343 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters
ترجمه فارسی عنوان
تشخیص خطای سنسور ترکیبی و بارگیری عملکردی برای سیستم های با پارامترهای مختلف زمان
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کلمات کلیدی
EAPPISACGMIDPMPRDIPSGFLWRSRRNaAFDRBGCEDRFPLSFDDsavitzky-golay filterANNPCA - PCAPrincipal component analysis - تحلیل مولفههای اصلی یا PCAFault detection and diagnosis - تشخیص گسل و تشخیصpartial least square - حداقل مربعات جزئیSensitivity - حساسیتLocally weighted regression - رگرسیون وزن محلیartificial neural networks - شبکه های عصبی مصنوعیGlucose concentration - غلظت گلوکزblood glucose concentration - غلظت گلوکز خونKalman filter - فیلتر کالمان یا فیلتر کالمنContinuous glucose monitoring - نظارت بر قند خون مداوم
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تکنولوژی و شیمی فرآیندی
چکیده انگلیسی
Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 60, December 2017, Pages 115-127
Journal: Journal of Process Control - Volume 60, December 2017, Pages 115-127
نویسندگان
Jianyuan Feng, Kamuran Turksoy, Sediqeh Samadi, Iman Hajizadeh, Elizabeth Littlejohn, Ali Cinar,