Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

Abstract The benefits of applying automated fault detection and diagnosis (AFDD) to chillers include less expensive repairs, timely maintenance, and shorter downtimes. This study employs feature selection (FS) techniques, such as mutual-information-based filter and genetic-algorithm-based wrapper, to help search for the important sensors in data driven chiller FDD applications, so as to improve FDD performance while saving initial sensor cost. The ‘one-against-one’ multi-class support vector machine (SVM) is adopted as a FDD tool. The results show that the eight features/sensors, centered around the core refrigeration cycle and selected by the GA-SVM wrapper from the original 64 features, outperform the other three feature subsets by the GA-LDA (linear discriminant analysis) wrapper, with an overall classification correct rate (CR) as high as 99.53% for the 4000 test samples randomly covering the normal and seven typical faulty modes. The CRs for the four cases with FS are all higher than that without FS (97.45%) and the test time is much less, about 28–36%. The FDD performance for normal or each of the faulty modes is also evaluated in details in terms of hit rate (HR) and false alarm rate (FAR).

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  P. Gruber,et al.  Qualitative model-based fault detection in air-handling units , 1995 .

[3]  Jeffrey M. Gordon,et al.  Centrifugal chillers: Thermodynamic modelling and a diagnostic case study , 1995 .

[4]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[5]  Hsuan-Tien Lin.  A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods , 2005 .

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

[7]  J. Klein Woud,et al.  On-line failure diagnosis for compression refrigeration plants , 1995 .

[8]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[9]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[10]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[13]  Sanmay Das,et al.  Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection , 2001, ICML.

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

[15]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[16]  Luis Puigjaner,et al.  Simultaneous fault diagnosis in chemical plants using a multilabel approach , 2007 .

[17]  Wojtek J. Krzanowski,et al.  Principles of multivariate analysis : a user's perspective. oxford , 1988 .

[18]  Bo Gu,et al.  PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems , 2010 .

[19]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[22]  George Kaler,et al.  Embedded Expert System Development for Monitoring Packaged HVAC Equipment , 1990 .

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

[24]  Cristian Ghiaus.  Fault diagnosis of air conditioning systems based on qualitative bond graph , 1999 .

[25]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[26]  K.R. Pattipati,et al.  Fault diagnosis in HVAC chillers , 2005, IEEE Instrumentation & Measurement Magazine.

[27]  Srinivas Katipamula,et al.  Diagnostics for Outdoor Air Ventilation and Economizers , 1998 .

[28]  Yiguang Liu,et al.  A novel and quick SVM-based multi-class classifier , 2006, Pattern Recognit..

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

[30]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.