Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features

Abstract Fault detection and diagnosis (FDD) of chillers, generally the single most energy consuming piece of building equipment, is an important but hard task where many parameters are involved, and the problem is always quite non-linear. This study proposes a least squares support vector machine (LS-SVM) model optimized by cross validation to implement FDD on a 90-ton centrifugal chiller. Four component-level and three system-level faults were investigated. The effectiveness and efficiency of the eight fault-indicative features extracted from the original 64 parameters have been validated and employed in the detailed discussions. The results indicated that as compared with the other two machine learning methods, the proposed LS-SVM model with optimization showed a better FDD performance in terms of the overall correct rate for all the samples, the individual correct rate for each fault, the diagnostic efficiency, the detection rate and the false alarm rate, etc., especially when it comes to the faults of system level where the detection and diagnosis become rather more difficult due to the system-level effect (SLE)—widespread symptoms caused throughout the system. The correct rates for system-level faults of refrigerant leak/undercharge, refrigerant overcharge and excessive oil were as high as 99.59%, 99.26% and 99.38%, respectively, and the running time was only 36.7% of that of the SVM model.

[1]  Huanxin Chen,et al.  Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with ReliefF filter , 2017 .

[2]  Ruxu Du,et al.  Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method , 2007 .

[3]  Savvas A. Tassou,et al.  Fault diagnosis and refrigerant leak detection in vapour compression refrigeration systems. , 2005 .

[4]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[5]  Ming Shao,et al.  HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[6]  Ting Wang,et al.  Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning , 2011 .

[7]  Shigeo Abe,et al.  Fuzzy least squares support vector machines for multiclass problems , 2003, Neural Networks.

[8]  Gaël Varoquaux,et al.  Cross-validation failure: Small sample sizes lead to large error bars , 2017, NeuroImage.

[9]  Mohammad M. Ghiasi,et al.  Modeling of gas hydrate phase equilibria: Extremely randomized trees and LSSVM approaches , 2017 .

[10]  Hua Han,et al.  Comparative study of probabilistic neural network and back propagation network for fault diagnosis of refrigeration systems , 2018 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Davood Dehestani.  HVAC Model Based Fault Detection by Incremental Online Support Vector Machine , 2013 .

[13]  Mahmood Al-khassaweneh,et al.  Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network , 2014, IEEE Transactions on Industrial Electronics.

[14]  Xiangyang Wang,et al.  A New Wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine , 2010, Expert Syst. Appl..

[15]  Jiong Li,et al.  A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system's refrigerant charge fault amount , 2016 .

[16]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[17]  Youming Chen,et al.  Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network , 2002 .

[18]  Indra Narayan Kar,et al.  Non-linear HVAC computations using least square support vector machines , 2009 .

[19]  H. C. Peitsman,et al.  Application of black-box models to HVAC systems for fault detection , 1996 .

[20]  Yunguang Gao,et al.  Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model , 2016 .

[21]  Xin Gao,et al.  An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process , 2016, Neurocomputing.

[22]  Youming Chen,et al.  An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts , 2016 .

[23]  Zhiwei Wang,et al.  Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary , 2016 .

[24]  Rongjing Hong,et al.  Degradation trend estimation of slewing bearing based on LSSVM model , 2016 .

[25]  Shen Zhenzhong,et al.  Prediction of the strength of concrete radiation shielding based on LS-SVM , 2015 .

[26]  Zhiwei Lian,et al.  Data mining based sensor fault diagnosis and validation for building air conditioning system , 2006 .

[27]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[28]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[29]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[30]  Shengwei Wang,et al.  Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD) , 2013 .