Conflict Intersection as Diagnostic Model

In this paper, we propose a new approach for feature selection using fuzzy-ARTMAP classification and conflict characterization in fault diagnosis process. This approach is realized in two stages. In the first one, we classify the unfaulty functioning data of system using the fuzzy-ARTMAP classification. In the second stage, a conflict is accounted between features of test data based on the hyper-cubes resulted in the first stage. Two features are in conflict if her intersection does not belong to the model elaborated by fuzzy-ARTMAP classification. This approach is applied with success in automotive application in which the relevant features are detected and isolated.

[1]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[2]  Keming Wang.  Neural Network Approach to Vibration Feature Selection and Multiple Fault Detection for Mechanical Systems , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[3]  Tshilidzi Marwala,et al.  Application of Feature Selection and Fuzzy ARTMAP to Intrusion Detection , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[5]  Randy J. Pell,et al.  Genetic algorithms combined with discriminant analysis for key variable identification , 2004 .

[6]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[8]  Thomas G. Dietterich,et al.  Learning Boolean Concepts in the Presence of Many Irrelevant Features , 1994, Artif. Intell..

[9]  Abdessamad Kobi,et al.  Fault detection and identification with a new feature selection based on mutual information , 2008 .

[10]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[11]  Terry R. Payne.  Dimensionality reduction and representation for nearest neighbour learning , 1999 .

[12]  Thomas G. Dietterich,et al.  Learning with Many Irrelevant Features , 1991, AAAI.

[13]  K. I. Ramachandran,et al.  Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing , 2007 .

[14]  Jianping Xuan,et al.  Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing , 2009, Expert Syst. Appl..

[15]  Gang Zhang,et al.  Weighted solution path algorithm of support vector regression for abnormal data , 2008, 2008 19th International Conference on Pattern Recognition.

[16]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[17]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[18]  Hervé Poulard.  Statistiques et réseaux de neurones pour un système de diagnostic : application au diagnostic de pannes automobiles. (Statistic and neural networks for a diagnosis system: Application to automotive failure detection) , 1996 .

[19]  Guy Clerc,et al.  The use of features selection and nearest neighbors rule for faults diagnostic in induction motors , 2006, Eng. Appl. Artif. Intell..

[20]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[21]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[22]  Justin Doak,et al.  An evaluation of feature selection methods and their application to computer security , 1992 .

[23]  Youxian Sun,et al.  Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection , 2007 .