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Fault Diagnosis Using Fuzzy Min-Max Neural Network Classifier

Suja S. Panicker, P. S. Dhabe, M. L. Dhore

Abstract


In this paper Fuzzy Min-Max Neural Network (FMN) classifier is used for Fault Diagnosis applications. It is a 3-layer architecture and uses a fuzzy membership function to reason about class label of a test pattern. We have collected two standard data sets- one from UCI repository and other from NASA, for experimentation purpose. Each data set is divided in two sets namely Training and Testing, using around half of the patterns. Above said Neural Network is trained using Training set and its performance is calculated using Test set. From the calculated performance it is found that the FMN performs well for both the data sets. By observing training, one can note that training time is more, but since training needs to be done only once it should not be treated as a serious handicap. Recall time per pattern is very small, thus the given neural network can be used for real time fault diagnostic purpose.

 


Keywords


Fault Diagnosis, Fuzzy Min Max Neural Network, NASA ADAPT data, UCI Pump data.

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References


Isermann, “Fault Diagnosis Systems – An introduction from Fault Detection to Fault Tolerance,” Springer , 2006, pp. 1-10

Patrick K. Simpson,“Fuzzy Min Max Neural Networks- Part 1 : Classification ,” IEEE Transactions on Neural Networks, Vol. 3, No. 5, September 1992

Zheng Zhang , Xinyu Shao , Daoyuan Yu, “Fault Diagnosis of a wheel Loader by Artificial Neural Networks and Fuzzy Logic,” IEEE 2006

Pan Lian , Tong Yao Bin , Ning Ning , Chen Aiping , “Application of Fuzzy Neural Network in Fault Diagnosis of Gasoline Engine,” The Ninth International Conference on Electronic Measurement & Instruments ICEMI’2009

Huaqing Wang, Peng Chen “Sequential Diagnosis for Rolling Bearing Using Fuzzy Neural Network,” Proceedings of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics July 2 - 5, 2008, Xi'an, China

Sheng Zhang, Toshiyuki Asakura, Xiaoli Xu, Baojie Xu, “Fault Diagnosis System for Rotary Machines Based on Fuzzy Neural Networks,” Proceedings of the 2003 IEEVASME International Conference on Advanced Intelligent Mechatronics (AIM 2003)

Massimo Meneganti, Francesco S. Saviello, and Roberto Tagliaferri, “Fuzzy Neural Networks for Classification and Detection of Anomalies,” IEEE Transactions on Neural Networks ,Vol. 9, No. 5, September 1998

Anna Wang, Junfang Liu, Hua Li, Feng Luan, Wenjing Yuan “A Novel Algorithm for Fault Diagnosis of Analog Circuit with Tolerances using improved Binary-tree SVMs Based on SOMNN Clustering,” Third International Conference on Natural Computation (ICNC 2007) , IEEE 2007

Letitia Mirea, Ron J. Patton, Senior Member IEEE, “Recurrent Wavelet Neural Networks Applied to Fault Diagnosis,”16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France June 25-27, 2008

Cai Lin, Huang Yuancan and Chen Jiabin “A Genetic-Based Fuzzy Clustering Algorithm for Fault Diagnosis in Satellite Attitude Determination System,” Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), IEEE 2006

Yin-Zhao Li, Chang-Zhen Hu, Kun-Sheng Wang, Li-Na Xu, Hui-Ling He, Jia-Dong Ren, “Information Entropy-Based Clustering Algorithm For Rapid Software Fault Diagnosis,” Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009 , IEEE 2009

Tao Xu, “Research on Sensor Fault Diagnosis Method based LVQ Neural Network and Clustering Analysis,” Proceedings of the 7th World Congress on Intelligent Control and Automation June 25 - 27, 2008, Chongqing, China ( IEEE 2008 )

Bogdan Gabrys and Andrzej Bargiela, “General Fuzzy Min-Max Neural Network for Clustering and Classification,” IEEE Transactions on Neural Networks, Vol. 11, No. 3, May 2000

Anas Quteishat, Chee Peng Lim and Kay Sin Tan “A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification,” IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 40, No. 3,May 2010

A. Rizzi , M. Panella, F. M. Frattale Mascioli , G. Martinelli, “Automatic Training of Generalized Min-Max Classifiers,” IEEE 2001

Antonello Rizzi, Massimo Panella, and Fabio Massimo Frattale Mascioli, “Adaptive Resolution Min-Max Classifiers,” IEEE Transactions on Neural Networks , Vol. 13, No. 2, March 2002

http://archive.ics.uci.edu/ml/machine-learning-databases/ mechanical-analysis/older-version/mechanical-analysis.names

https://dashlink.arc.nasa.gov/data/adapt-an-electrical-power-system-testbed/

http://gp-you.org


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