کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947923 | 1439599 | 2017 | 28 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Automatic, accurate and online fault detection of heating ventilation air conditioning (HVAC) subsystems, such as chillers, is highly demanded in building management system (BMS) to prevent energy waste and high maintenance cost. However, most fault detection techniques require rich faulty training data which is usually unavailable. In this study, a novel hybrid method is proposed to detect faults for chiller subsystems without any faulty training data available, i.e. by training the normal data only. A hybrid feature selection algorithm is applied to the chiller dataset collected by ASHRAE project 1043-RP to select the most significant feature variables. An online classification framework is introduced by combining an Extended Kalman Filter (EKF) model and a recursive one-class support vector machine (ROSVM). Experiment results show that the proposing algorithm detects typical chiller faults with high accuracy rates and requires less feature variables compared to existing works.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 228, 8 March 2017, Pages 205-212
Journal: Neurocomputing - Volume 228, 8 March 2017, Pages 205-212
نویسندگان
Ke Yan, Zhiwei Ji, Wen Shen,