Fault diagnosis strategy for incompletely described samples and its application to refrigeration system

Abstract Fault diagnosis (FD) plays a very important role in the operation and maintenance of mechanical system and equipment. Existing FD methods are not capable of effectively dealing with incompletely described samples. In this paper, a strategy for FD using the incompletely described samples is presented. It is actualized in two steps, namely the determination of the values of unknown features which is the key step of the presented FD strategy, and the utilization of the regenerated completely described samples to diagnose the system based on support vector machine (SVM) classifiers. And the first step is mainly implemented by the following three sub-steps: (1) with the help of domain knowledge, the similarity transformation matrix of partial problem description (PPD)—problems with incomplete feature description—is generated based on the historical database; (2) the unknown features of the samples are transformed to related known features, through which generates a new retrieval feature vector; (3) the values of unknown features are assigned by the optimal cases which can be retrieved by measuring and comparing similarities between the retrieval feature vector and the completely described samples in the historical database. Finally, the presented FD strategy was applied to a real refrigeration system, and achieved satisfying results.

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

[2]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

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

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

[5]  Hani G. Melhem,et al.  Monitoring bridge health using fuzzy case-based reasoning , 2005, Adv. Eng. Informatics.

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

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

[8]  Wenjie Hu,et al.  Robust support vector machine with bullet hole image classification , 2002 .

[9]  Steven Bogaerts,et al.  Facilitating CBR for Incompletely-Described Cases: Distance Metrics for Partial Problem Descriptions , 2004, ECCBR.

[10]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[11]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[12]  Fulei Chu,et al.  Support vector machines-based fault diagnosis for turbo-pump rotor , 2006 .

[13]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[14]  Guangyi Cao,et al.  Nonlinear modeling of a SOFC stack based on a least squares support vector machine , 2006 .

[15]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

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

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

[18]  Ahmet Palazoglu,et al.  Classification of abnormal plant operation using multiple process variable trends , 2001 .

[19]  Krishna R. Pattipati,et al.  Fault detection, diagnosis, and data-driven modeling in HVAC chillers , 2005, SPIE Defense + Commercial Sensing.

[20]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[21]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

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

[23]  Parisa A. Bahri,et al.  Integration techniques in intelligent operational management: a review , 2005, Knowl. Based Syst..

[24]  Chih-Ming Chen,et al.  A self-organizing HCMAC neural-network classifier , 2003, IEEE Trans. Neural Networks.

[25]  D. Anguita,et al.  A new method for multiclass support vector machines , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[26]  Cheng Haozhong,et al.  Fault diagnosis of power transformer based on multi-layer SVM classifier , 2005 .

[27]  Dongkon Lee,et al.  An approach to case-based system for conceptual ship design assistant , 1999 .

[28]  Natascha S. Milesi-Ferretti.  Performance Evaluation of a Reciprocating Chiller Using Experimental Data and Model Predictions for Fault Detection and Diagnosis | NIST , 2002 .