NDLI: A Comparison between Polynomial and Locally Weighted Regression for Fault Detection and Diagnosis of HVAC Equipment

Content Provider	IEEE Xplore Digital Library
Author	Radhakrishnan, R. Nikovski, D. Peker, K. Divakaran, A.
Copyright Year	2006
Description	Author affiliation: Mitsubishi Electr. Res. Labs., Cambridge, MA (Radhakrishnan, R.; Nikovski, D.; Peker, K.; Divakaran, A.)
Abstract	We investigate the accuracy of two predictive modeling methods for the purpose of fault detection and diagnosis (FDD) for HVAC equipment. The comparison is performed within an FDD framework consisting of two steps. In the first step, a predictive regression model is built to represent the dependence of the internal state variables of the HVAC device on the external driving influences, under normal operating conditions. This regression model obtained from training data is used to predict expected readings for state variables, and compute deviations from these readings under various abnormal conditions. The objective of the second step in the FDD framework is to learn to detect abnormalities based on regularities in computed deviations (residuals) from normal conditions. The accuracy of the first step (regression) is essential to the success of this method, since it disambiguates whether variations in observed state variables are due to faults or external driving conditions. In this paper, we present a comparison between locally weighted regression (a local non-linear model) and polynomial regression (a global non-linear model) in the context of fault detection and diagnosis of HVAC equipment. Our experimental results for detection and diagnosis of "overcharged" and "undercharged" refrigerant conditions in an HVAC device show that locally weighted regression clearly outperforms polynomial regression for this task
Starting Page	3668
Ending Page	3673
File Size	183114
Page Count	6
File Format	PDF
ISBN	1424403901
ISSN	1553572X
DOI	10.1109/IECON.2006.347601
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-06
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Polynomials Fault detection Fault diagnosis Predictive models Electrical fault detection Context modeling State estimation Machine learning Accuracy Training data
Content Type	Text
Resource Type	Article

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A Comparison Between Polynomial and Locally Weighted Regression for Fault Detection and Diagnosis of HVAC Equipment (2006)

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