An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

Abstract This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

[1]  Fu Xiao,et al.  Bayesian network based FDD strategy for variable air volume terminals , 2014 .

[2]  Ke Yan,et al.  Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM , 2017, Neurocomputing.

[3]  Huanxin Chen,et al.  Optimized neural network-based fault diagnosis strategy for VRF system in heating mode using data mining , 2017 .

[4]  Bart De Schutter,et al.  Combining knowledge and historical data for system-level fault diagnosis of HVAC systems , 2017, Eng. Appl. Artif. Intell..

[5]  Xinhua Xu,et al.  An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems , 2009 .

[6]  Yang Zhao,et al.  Diagnostic Bayesian networks for diagnosing air handling units faults, Part II::Faults in coils and sensors , 2015 .

[7]  Zhiwei Wang,et al.  Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications , 2018 .

[8]  Huanxin Chen,et al.  An enhanced PCA method with Savitzky-Golay method for VRF system sensor fault detection and diagnosis , 2017 .

[9]  Youming Chen,et al.  A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units , 2016 .

[10]  Jinhua Wang,et al.  Online model-based fault detection and diagnosis strategy for VAV air handling units , 2012 .

[11]  Prashant Mhaskar,et al.  Heating, ventilation and air conditioning systems: Fault detection and isolation and safe parking , 2018, Comput. Chem. Eng..

[12]  Zhiwei Wang,et al.  Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information , 2017 .

[13]  Fu Xiao,et al.  A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers , 2011 .

[14]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .

[15]  Liping Wang,et al.  Fault detection and diagnosis for nonlinear systems: A new adaptive Gaussian mixture modeling approach , 2018 .

[16]  Yuebin Yu,et al.  A review of fault detection and diagnosis methodologies on air-handling units , 2014 .

[17]  Seo Young Kim,et al.  Indoor unit fault detector for a multi-split VRF system in heating mode , 2014 .

[18]  S. Friedrich Energy Efficiency in Buildings in EU Countries , 2013 .

[19]  Bryan P. Rasmussen,et al.  A comparison of static and dynamic fault detection techniques for transcritical refrigeration , 2017 .

[20]  Biswajit Basu,et al.  Residential HVAC fault detection using a system identification approach , 2017 .

[21]  Ling Chen,et al.  Data-driven based reliability evaluation for measurements of sensors in a vapor compression system , 2017 .

[22]  Jin Wen,et al.  Diagnostic Bayesian networks for diagnosing air handling units faults – part I: Faults in dampers, fans, filters and sensors , 2017 .

[23]  Bing Dong,et al.  A probabilistic approach to diagnose faults of air handling units in buildings , 2016 .

[24]  Manel Martínez-Ramón,et al.  Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models , 2017 .

[25]  Dongqing Xie,et al.  Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis. , 2018 .

[26]  James E. Braun,et al.  Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics , 2016 .