Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach

2020 ◽  
Vol 118 ◽  
pp. 1-11 ◽  
Author(s):  
Yabin Guo ◽  
Huanxin Chen
2019 ◽  
Vol 68 (12) ◽  
pp. 4746-4755 ◽  
Author(s):  
Youngsun Hong ◽  
Minsu Kim ◽  
Hyunho Lee ◽  
Jong Jin Park ◽  
Dongyeon Lee

2021 ◽  
Vol 69 (6) ◽  
pp. 538-549
Author(s):  
Ayla Nawaz ◽  
Christian Herzog né Hoffmann ◽  
Jan Graßhoff ◽  
Sven Pfeiffer ◽  
Gerwald Lichtenberg ◽  
...  

Abstract The European X-ray Free Electron Laser (EuXFEL) is a complex system with many interconnected components and sensor measurements. We use factor graphs to systematically design a probabilistic fault diagnosis method for its cavity system. This approach is expandable to further subsystems and considers uncertainties from measurements and modeling. After representing a model of the cavity system in the factor graph framework, we infer marginal distributions, e. g., of the fault classes using tabulated message-passing definitions. The emerging fault diagnosis method consists of an unscented Kalman filter-based residual generator and an evaluation of the residuals using a Gaussian mixture model. We include message-passing definitions for the training of the Gaussian Mixture Model from noisy data using the expectation-maximization algorithm.


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