A comparison between polynomial and locally weighted regression for fault detection and diagnosis of HVAC equipment

IECON Proceedings (Industrial Electronics Conference)

Volumn , Issue , 2006, Pages 3668-3673

  Radhakrishnan, Regunathan ^a   Nikovski, Daniel ^a   Peker, Kadir ^a   Divakaran, Ajay ^a  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR CONDITIONING; ELECTRONICS INDUSTRY; FAULT DETECTION; FOOD PROCESSING; FREQUENCY DIVISION MULTIPLEXING; INDUSTRIAL ELECTRONICS; LEARNING SYSTEMS; MATHEMATICAL MODELS; ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING; POLYNOMIAL APPROXIMATION; POLYNOMIALS;

ABNORMAL CONDITIONS; ANNUAL CONFERENCE; COMPUTED DEVIATIONS; EXTERNAL DRIVING; FAULT DETECTION AND DIAGNOSIS; HVAC EQUIPMENT; INTERNAL STATE VARIABLES; LOCALLY WEIGHTED REGRESSION; NON-LINEAR MODELING; NORMAL OPERATING CONDITIONS; POLYNOMIAL REGRESSIONS; PREDICTIVE MODELING; REGRESSION MODELLING; STATE VARIABLES; TRAINING DATA;

REGRESSION ANALYSIS;

EID: 50249099128     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IECON.2006.347601     Document Type: Conference Paper

References (6)

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