CIESC Journal ›› 2018, Vol. 69 ›› Issue (7): 3167-3173.doi: 10.11949/j.issn.0438-1157.20180003

Previous Articles     Next Articles

Chiller fault diagnosis based on fusional Bayesian network

WANG Zhanwei1, WANG Lin1, LIANG Kunfeng1, YUAN Junfei1, WANG Zhiwei2   

  1. 1 Institute of Refrigeration, Heat Pump, and Air Conditioning, Henan University of Science and Technology, Luoyang 471023, Henan, China;
    2 School of Environment, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China
  • Received:2018-01-02 Revised:2018-03-12 Online:2018-07-05 Published:2018-04-09
  • Supported by:

    supported by the National Natural Science Foundation of China (51641604, U1504524).


Based on a open network topology of Bayesian network (BN), on-site observed information is fused into BN to improve the fault diagnostic performances. A mechanism of distance rejection is introduced to determine the probability distribution of sensor measurement parameters. A chiller fault diagnosis method based on fusional BN is proposed. This method is able to detect new types of chiller fault and update its fault library dynamically. Use the experimental data from ASHRAE RP-1043 to evaluate the performances of the proposed method. The results show that the accuracy of the new type of fault (NF1) is 99.8%, and fusing on-site observed information increases the detection accuracies of the new types of fault (NF2) by 32.6% and the diagnostic accuracies of known fault rl and ro by 4.8% and 11.2% respectively.

Key words: chiller, fault diagnosis, algorithm, control, integration

CLC Number: 

  • TB65

[1] SAIDUR R, HASANUZZAMAN M, MAHLIA T M I, et al. Chillers energy consumption, energy savings and emission analysis in an institutional buildings[J]. Energy, 2011, 36(8):5233-5238.
[2] 谷波, 韩华, 洪迎春, 等. 基于SVM的制冷系统多故障并发检测与诊断[J]. 化工学报, 2011, 62(S2):112-119. GU B, HAN H, HONG Y C, et al. SVM-based FDD of multiplesimultaneous faults for chillers[J]. CIESC Journal, 2011, 62(S2):112-119.
[3] HAN H, GU B, KANG J, et al. Study on a hybrid SVM model for chiller FDD applications[J]. Applied Thermal Engineering, 2011, 31(4):582-592.
[4] 顾笑伟, 王智伟, 王占伟, 等. 基于密度权重支持向量数据描述的冷水机组故障检测[J]. 化工学报, 2017, 68(3):1099-1108. GU X W, WANG Z W, WANG Z W, et al. Chiller fault detection by density weighted support vector data description[J]. CIESC Journal, 2017, 68(3):1099-1108.
[5] 李冠男, 胡云鹏, 陈焕新, 等. 基于SVDD的冷水机组传感器故障检测及效率分析[J]. 化工学报, 2015, 66(5):1815-1820. LI G N, HU Y P, CHEN H X, et al. SVDD-based chiller sensor fault detection method and its detection efficiency[J]. CIESC Journal, 2015, 66(5):1815-1820.
[6] ZHAO X Z, YANG M, LI H R. Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP)[J]. HVAC&R Research, 2011, 17(1):86-106.
[7] DU Z M, FAN B, JIN X Q, et al. Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis[J]. Building and Environment, 2014, 73:1-11.
[8] ZHU Y H, JIN X Q, DU Z M. Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal[J]. Energy and Buildings, 2012, 44:7-16.
[9] TRAN D A T, CHEN Y M, CHAU M Q, et al. A robust online fault detection and diagnosis strategy of centrifugal chiller systems for building energy efficiency[J]. Energy and Buildings, 2015, 108:441-453.
[10] GUO Y B, LI G N, CHEN H X, et al. Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults[J]. Applied Thermal Engineering, 2017, 115:744-755.
[11] LI G N, HU Y P, CHEN H X, et al. An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm[J]. Energy and Buildings, 2016, 116:104-113.
[12] PEARL J. Fusion, propagation, and structuring in belief networks[J]. Artificial Intelligence, 1986, 29(3):241-288.
[13] NAJAFI M, AUSLANDER D M, BARTLETT P L, et al. Application of machine learning in the fault diagnostics of air handling units[J]. Applied Energy, 2012, 96:347-358.
[14] WALL J, GUO Y, LI J M, et al. A dynamic machine learning-based technique for automated fault detection in HVAC systems[J].ASHRAE Transactions, 2011, 117(2):449-456.
[15] ZHAO Y, XIAO F, WANG S W. An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network[J]. Energy and Buildings, 2013, 57:278-288.
[16] ZHAO Y, WEN J, XIAO F, et al. Diagnostic Bayesian networks for diagnosing air handling units faults(I):Faults in dampers, fans, filters and sensors[J]. Applied Thermal Engineering, 2017, 111:1272-1286.
[17] XIAO F, ZHAO Y, WEN J, et al. Bayesian network based FDD strategy for variable air volume terminals[J]. Automation in Construction, 2014, 41:106-118.
[18] CAI B P, LIU Y H, FAN Q, et al. Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network[J]. Applied Energy, 2014, 114:1-9.
[19] HE S W, WANG Z W, WANG Z W, et al. Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary[J]. Applied Thermal Engineering, 2016, 107:37-47.
[20] WANG Z W, WANG Z W, GU X W, et al. Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications[J]. Applied Thermal Engineering, 2018, 129:674-683.
[21] MADSEN A L. Belief update in CLG Bayesian networks with lazy propagation[J]. International Journal of Approximate Reasoning, 2008, 49(2):503-521.
[22] VERRON S, TIPLICA T, KOBI A. Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion[J]. Engineering Applications of Artificial Intelligence, 2010, 23(7):1229-1235.
[23] ATOUI M A, VERRON S, KOBI A. Fault detection with conditional Gaussian network[J]. Engineering Applications of Artificial Intelligence, 2015, 45:473-481.
[24] COMSTOCK M C, BRAUN J E. Development of analysis tools for the evaluation of fault detection and diagnostics for chillers[R]. ASHRAE Research Project 1043-RP, HL 99-20, Report #4036-3, 1999.
[25] BRAUN J E. Automated fault detection and diagnostics for vapor compression cooling equipment[J]. Journal of Solar Engineering, 2003, 125(3):266-274.
[26] ZHAO X Z, YANG M, LI H R. Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers[J]. Energy and Buildings, 2014, 72:419-430.
[27] ZHAO Y, XIAO F, WEN J, et al. A robust pattern recognition-based fault detection and diagnosis (FDD) method for chillers[J]. HVAC&R Research, 2014, 20(7):798-809.
[28] KIM M, YOON S H, DOMANSKI P A, et al. Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner[J]. International Journal of Refrigeration, 2008, 31(5):790-799.
[29] REDDY T A. Formulation of a generic methodology for assessing FDD methods and its specific adoption to large chillers[J]. ASHRAE Transactions, 2007, 113(2):334-342.
[30] COMSTOCK M C, BRAUN J E, GROLL E A. The sensitivity of chiller performance to common faults[J]. HVAC&R Research, 2001, 7(3):263-279.

[1] XU Qichao, JIANG Jinbo, PENG Xudong, LI Jiyun, WANG Yuming. Unified model and geometrical optimization of bi-directional groove of dry gas seal based on genetic algorithm [J]. CIESC Journal, 2019, 70(3): 995-1005.
[2] SHI Bowen, YIN Yanyan, LIU Fei. Optimal control strategies combined with PSO and control vector parameterization for batchwise chemical process [J]. CIESC Journal, 2019, 70(3): 979-986.
[3] WANG Zhizhen, ZOU Zhiyun. Nonlinear predictive control strategies of pH neutralization process based on neural networks [J]. CIESC Journal, 2019, 70(2): 678-686.
[4] LIU Qilei, FENG Kun, LIU Linlin, DU Jian, MENG Qingwei, ZHANG Lei. Reaction solvent design method based on Dragon descriptors and modified decision tree-genetic algorithm [J]. CIESC Journal, 2019, 70(2): 533-540.
[5] GUO Xiaozheng, LIU Linlin, ZHANG Lei, DU Jian. Property integration of batch process based on interceptors in semi-continuous operation [J]. CIESC Journal, 2019, 70(2): 516-524.
[6] HUANG Xiuhui, WANG Jun, CUI Guomin. Dynamic simulation and analysis of control strategies of acetic acid dehydration tower in PTA plant [J]. CIESC Journal, 2019, 70(2): 625-633.
[7] YE Zhencheng, ZHOU Huanlan, RAO Debao. Hybrid modeling and optimization of acetylene hydrogenation process [J]. CIESC Journal, 2019, 70(2): 496-507.
[8] WU Changhao, LIU Linlin, ZHANG Lei, DU Jian. Inter-plant waste heat integration for industrial park using two medium fluids [J]. CIESC Journal, 2019, 70(2): 431-439.
[9] JI Wenpeng, YANG Huizhong. Multi-manifold soft sensor based on modified expanding search clustering algorithm [J]. CIESC Journal, 2019, 70(2): 723-729.
[10] DOU Shan, ZHANG Guangyu, XIONG Zhihua. Anomaly detection of process unit based on LSTM time series reconstruction [J]. CIESC Journal, 2019, 70(2): 481-486.
[11] GU Bingbin, XIONG Weili. Fault diagnosis based on PCA method with multi-block information extraction [J]. CIESC Journal, 2019, 70(2): 736-749.
[12] PENG Kaixiang, ZHANG Chuanfang, MA Liang, DONG Jie, JIAO Ruihua, TANG Peng. System-levels-based holographic fault diagnosis for complex industrial processes [J]. CIESC Journal, 2019, 70(2): 590-598.
[13] BAI Junren, YI Jun, LI Qian, WU Ling, CHEN Xuemei. Multi-objective optimization of QPSO for thereaction-regeneration process [J]. CIESC Journal, 2019, 70(2): 750-756.
[14] GENG Zhiqiang, JING Shaoxing, BAI Ju, WANG Zhongkai, ZHU Qunxiong, HAN Yongming. Improved intelligent warning method based on MWSPCA-CBR and its application in petrochemical industries [J]. CIESC Journal, 2019, 70(2): 572-580.
[15] XUE Yongfei, WANG Yalin, SUN Bei, LI Qianzhong, SUN Jiazhou. Improved state transfer algorithm-based kinetics parameter estimation for cascaded plug flow reactors [J]. CIESC Journal, 2019, 70(2): 607-616.
Full text



No Suggested Reading articles found!