Performance investigation of a variable speed vapor compression system for fault detection and diagnosis

2005 ◽  
Vol 28 (4) ◽  
pp. 481-488 ◽  
Author(s):  
Minsung Kim ◽  
Min Soo Kim
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Vapor Compression ◽  
Variable Speed ◽  
Compression System ◽  
Vapor Compression System ◽  
Detection And Diagnosis

Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment

2003 ◽  
Vol 125 (3) ◽  
pp. 266-274 ◽  
Author(s):  
James E. Braun
Keyword(s):  
Fault Detection ◽  
Case Studies ◽  
State Of The Art ◽  
General Structure ◽  
Fault Detection And Diagnosis ◽  
Vapor Compression ◽  
Widespread Application ◽  
Air Conditioners ◽  
Detection And Diagnosis ◽  
Vapor Compression Cooling

This paper provides an overview of research related to automated fault detection and diagnosis for chillers, packaged air conditioners, and other vapor compression cooling equipment. The paper discusses the benefits, constraints, and possible products for FDD applied in the HVAC&R industry, presents results of fault surveys for packaged air conditioners and chillers, outlines the general structure and elements of an FDD system for HVAC&R equipment, describes specific methods associated with different FDD elements, and presents results from some specific case studies. The paper also attempts to provide an assessment of the state-of-the-art in FDD for vapor compression equipment and to identify the steps necessary to achieve widespread application.

scholarly journals Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique Under Constant and Variable Speed Conditions

2018 ◽  
Vol 8 (8) ◽  
pp. 1392 ◽  
Author(s):  
Moussa Hamadache ◽  
Dongik Lee ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz
Keyword(s):  
Fault Detection ◽  
Image Recognition ◽  
Fault Detection And Diagnosis ◽  
Variable Speed ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Detection And Diagnosis ◽  
Recognition Technique

This paper addresses the application of an image recognition technique for the detection and diagnosis of ball bearing faults in rotating electrical machines (REMs). The conventional bearing fault detection and diagnosis (BFDD) methods rely on extracting different features from either waveforms or spectra of vibration signals to detect and diagnose bearing faults. In this paper, a novel vibration-based BFDD via a probability plot (ProbPlot) image recognition technique under constant and variable speed conditions is proposed. The proposed technique is based on the absolute value principal component analysis (AVPCA), namely, ProbPlot via image recognition using the AVPCA (ProbPlot via IR-AVPCA) technique. A comparison of the features (images) obtained: (1) directly in the time domain from the original raw data of the vibration signals; (2) by capturing the Fast Fourier Transformation (FFT) of the vibration signals; or (3) by generating the probability plot (ProbPlot) of the vibration signals as proposed in this paper, is considered. A set of realistic bearing faults (i.e., outer-race fault, inner-race fault, and balls fault) are experimentally considered to evaluate the performance and effectiveness of the proposed ProbPlot via the IR-AVPCA method.

Steady-state model of a variable speed vapor compression system using R134a as working fluid

2009 ◽  
Vol 34 (11) ◽  
pp. 933-945 ◽  
Author(s):  
J. M. Belman ◽  
J. Navarro-Esbrí ◽  
D. Ginestar ◽  
V. Milian
Keyword(s):  
Steady State ◽  
State Model ◽  
Working Fluid ◽  
Vapor Compression ◽  
Variable Speed ◽  
Compression System ◽  
Steady State Model ◽  
Vapor Compression System

Analysis of a variable speed vapor compression system using artificial neural networks

2013 ◽  
Vol 40 (11) ◽  
pp. 4362-4369 ◽  
Author(s):  
J.M. Belman-Flores ◽  
S.E. Ledesma ◽  
M.G. Garcia ◽  
J. Ruiz ◽  
J.L. Rodríguez-Muñoz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vapor Compression ◽  
Variable Speed ◽  
Compression System ◽  
Vapor Compression System ◽  
Artificial Neural
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