Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jul 14, 2017
Bo Zheng
Hong-Zhong Huang
Jie Zhou
Yan-Feng Li
Abstract
To improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the graph partition based on dimensionless similarity (GPDS) method is proposed to overcome the influence of attributes with different dimensions. The algorithm is named graph partition based on dimensionless similarity. Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Meanwhile, a weighted relative error is proposed to measure quantitatively the distribution change of original data after being compressed. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly
##plugins.themes.bootstrap3.article.details##
Keywords
Granular Computing, Graph Partition, Dimensionless Similarity, Weighted Relative Error, Fault Diagnosis
References
Airteimoori, A., & Kordrostami, S. (2010). A euclidean distance-based measure of efficiency in data envelopment analysis. Optimization, vol.59, pp.985-996.
Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration. Applied Acoustics, vol.89, pp.16-27.
Anjum, R. A., Zhang, Y. Q., Harrison, R. W. (2009). Granular decision tree and evolutionary neural SVM for protein secondary structure predictio. International Journal of Computational Intelligence Systems, vol2(4), pp.343-352.
Biswal, B., Biswal, M., Hasan, S., and Dash, P. K. (2014). Nonstationary power signal time series data classification using LVQ classifier. Applied Soft Computing, vol.18, pp.158-166.
D'Orsi, C. J. 2001. Computer-aided detection: there is no free lunch. Radiology, vol.221, pp.585-586.
Huang, M., Wang, J., & Liang, X. (2007). Genetic algorithm controlled by two thresholds for job scheduling problem. Computer Integrated Manufacturing System, vol.13, pp.329-332.
Li, Y. (2007). The computer networ routing research based on granularity computation intelligence. Hefei: Anhui University.
Liu, X. C. (1992). Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems, vol.52, pp.305-318.
Neto, A. M. (2014). Pearson’s correlation coefficient: a more realistic threshold for applications on autonomous robotics. Computer Technology and Application, vol.5, pp.69-72.
Park, H. S., Pedrycz, W., Oh, S. K. (2009). Granular neural networks and their development through context-based clustering and adjustable dimensionality of receptive fields. IEEE Transactions on Neural Networks, vol.20,
pp.1604-1016.
Pawlak, Z. (1991). Rough sets-theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers.
Skowron, A., & Stepaniuk, J. (2001). Information granules: towards foundations of granular computin. International Journal of Intelligent Systems, vol.16, pp. 57-85.
Tang, Y. C., Jin, B., & Zhang, Y. Q. (2005). Granular support vector machines with association rules mining for protein homology prediction. Artificial Intelligence in Medicion, vol.35, pp.121-134.
von Luvburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, vol.17, pp.395-416.
Xiang, S. X., Nie, F. P., Zhang, C. S. (2008). Learning a mahalanobis distance metric for data clustering and classification. Pattern Recognition, vol.41, pp.3600-3612.
Ye, J. (2011). Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modeling, vol.53, pp.91-97.
Zadeh, L. A. (1996). Fuzzy logic-computing with words. IEEE Transactions on Fuzzy System, vol.4, pp.103-111.
Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets & Systems, vol.90, pp.111-117.
Zhang, B., & Zhang, L. (1992). Theory and application of problem solving. North-Holland: Elsevisr Science Publisher.
Zhang, B., & Zhang. L. (2003). Theory of fuzzy quotient space (methods of fuzzy granular computing). Journal of Software, vol.14, pp.770-776.
Zhang, X. (2000). Research on granular support vector machine. Taiyuan: Shanxi University.
Zhang, Y. P., Zhang, L., & Wu, T. (2004). The representation of different granular worlds: a quotient space. Chinese Journal of Computers, vol.27, pp.328-333.
Zhao, X. L., & Yang, Y. (2007). Development status and application research on granular computing. Computer Engineering, vol.33, pp.101-103.
Zheng, B. (2013). Investigation on aeroengine maintenance level decision on PSO-SVM. Journal of Propulsion Technology, vol.34, pp.687-692
Zheng, B., & Gao, F. (2015). Fault diagnosis method based on S-PSO classification algorithm. Acta Aeronautica et Astronautica Sinica, vol.36, pp.3640-3651.
Zheng, B., Huang, H. Z., & Li, Y. F. (2015). Aeroengine fault diagnosis method based on optimized supervised kohonen network. Journal of Donghua University, vol.32, pp.1029-1033.
Zheng, B., Huang, H. Z., Xu, H. W., Meng, D. B., and Zhang, X. L. (2016). Multi-team competitive optimization algorithm and its application in bearing fault diagnosis. 2016 Annual Reliability and Maintainability Symposium.
Zhong, W., He, J. Y., Harrison, R., Tai, P. C., and Pan, Y. 2007. Clustering support vector machines for protein local structure prediction. Expert Systems with Application, vol.32, pp.518-526.
Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., and Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration. Applied Acoustics, vol.89, pp.16-27.
Anjum, R. A., Zhang, Y. Q., Harrison, R. W. (2009). Granular decision tree and evolutionary neural SVM for protein secondary structure predictio. International Journal of Computational Intelligence Systems, vol2(4), pp.343-352.
Biswal, B., Biswal, M., Hasan, S., and Dash, P. K. (2014). Nonstationary power signal time series data classification using LVQ classifier. Applied Soft Computing, vol.18, pp.158-166.
D'Orsi, C. J. 2001. Computer-aided detection: there is no free lunch. Radiology, vol.221, pp.585-586.
Huang, M., Wang, J., & Liang, X. (2007). Genetic algorithm controlled by two thresholds for job scheduling problem. Computer Integrated Manufacturing System, vol.13, pp.329-332.
Li, Y. (2007). The computer networ routing research based on granularity computation intelligence. Hefei: Anhui University.
Liu, X. C. (1992). Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets and Systems, vol.52, pp.305-318.
Neto, A. M. (2014). Pearson’s correlation coefficient: a more realistic threshold for applications on autonomous robotics. Computer Technology and Application, vol.5, pp.69-72.
Park, H. S., Pedrycz, W., Oh, S. K. (2009). Granular neural networks and their development through context-based clustering and adjustable dimensionality of receptive fields. IEEE Transactions on Neural Networks, vol.20,
pp.1604-1016.
Pawlak, Z. (1991). Rough sets-theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers.
Skowron, A., & Stepaniuk, J. (2001). Information granules: towards foundations of granular computin. International Journal of Intelligent Systems, vol.16, pp. 57-85.
Tang, Y. C., Jin, B., & Zhang, Y. Q. (2005). Granular support vector machines with association rules mining for protein homology prediction. Artificial Intelligence in Medicion, vol.35, pp.121-134.
von Luvburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, vol.17, pp.395-416.
Xiang, S. X., Nie, F. P., Zhang, C. S. (2008). Learning a mahalanobis distance metric for data clustering and classification. Pattern Recognition, vol.41, pp.3600-3612.
Ye, J. (2011). Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modeling, vol.53, pp.91-97.
Zadeh, L. A. (1996). Fuzzy logic-computing with words. IEEE Transactions on Fuzzy System, vol.4, pp.103-111.
Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets & Systems, vol.90, pp.111-117.
Zhang, B., & Zhang, L. (1992). Theory and application of problem solving. North-Holland: Elsevisr Science Publisher.
Zhang, B., & Zhang. L. (2003). Theory of fuzzy quotient space (methods of fuzzy granular computing). Journal of Software, vol.14, pp.770-776.
Zhang, X. (2000). Research on granular support vector machine. Taiyuan: Shanxi University.
Zhang, Y. P., Zhang, L., & Wu, T. (2004). The representation of different granular worlds: a quotient space. Chinese Journal of Computers, vol.27, pp.328-333.
Zhao, X. L., & Yang, Y. (2007). Development status and application research on granular computing. Computer Engineering, vol.33, pp.101-103.
Zheng, B. (2013). Investigation on aeroengine maintenance level decision on PSO-SVM. Journal of Propulsion Technology, vol.34, pp.687-692
Zheng, B., & Gao, F. (2015). Fault diagnosis method based on S-PSO classification algorithm. Acta Aeronautica et Astronautica Sinica, vol.36, pp.3640-3651.
Zheng, B., Huang, H. Z., & Li, Y. F. (2015). Aeroengine fault diagnosis method based on optimized supervised kohonen network. Journal of Donghua University, vol.32, pp.1029-1033.
Zheng, B., Huang, H. Z., Xu, H. W., Meng, D. B., and Zhang, X. L. (2016). Multi-team competitive optimization algorithm and its application in bearing fault diagnosis. 2016 Annual Reliability and Maintainability Symposium.
Zhong, W., He, J. Y., Harrison, R., Tai, P. C., and Pan, Y. 2007. Clustering support vector machines for protein local structure prediction. Expert Systems with Application, vol.32, pp.518-526.
Section
Regular Session Papers