کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
263656 504080 2012 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
Chiller sensor fault detection using a self-Adaptive Principal Component Analysis method
چکیده انگلیسی

This paper presents a self adaptive chiller sensor fault detection strategy based on Principal Component Analysis (PCA) method, namely a self-Adaptive Principal Component Analysis (APCA) method. The original data set used to train the PCA model usually contains some error samples, whose useless residual subspace information make the threshold of Q-statistic higher than the expected threshold. This leads to a low sensitivity and low fault detection efficiency at low sensor fault levels. APCA is developed to automatically remove error samples in the original data set in order to improve fault detection efficiency especially for temperature sensor faults with absolute magnitude less than 1 °C. The self adaptive process of APCA has been presented and been compared with the Normal Principal Component Analysis (NPCA) method. The APCA strategy is validated by the operational data of a screw chiller system in a real electric factory. The results show that the APCA method can significantly enhance the fault detection efficiency, and the symmetry for positive and negative fault levels with same absolute magnitude becomes better than NPCA method.


► A self-Adaptive PCA sensor FDD strategy for screw chillers is developed.
► The detection efficiency is enhanced significantly especially in low fault levels.
► The detection symmetry of positive and negative fault levels is promoted.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy and Buildings - Volume 54, November 2012, Pages 252–258
نویسندگان
, , , , ,