Fault diagnosis of civil aircraft electrical system based on evidence theory

Dempster-Shafer(D-S) method has been used widely in fault diagnosis system of civil aircraft electrical system, but it has difficulty in dealing with combining evidences with high degree of conflict. In order to solve the problem, a new method is proposed in this paper. The proposed method in this paper introduces the historical data, defines the concept of modifying factor, and considers the influence of expert knowledge to basic probability assignments. The experimental results show that the new method can improve the reliability and accuracy of fault diagnosis results and enhance the performance of the system. This method is a breakthrough in the engineering application of D-S evidence theory.

[1]  Yu Luo,et al.  An improved method to rank generalized fuzzy numbers with different left heights and right heights , 2015, J. Intell. Fuzzy Syst..

[2]  Yang Liu,et al.  An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP , 2015 .

[3]  Richard W. Jones,et al.  A framework for intelligent medical diagnosis using the theory of evidence , 2002, Knowl. Based Syst..

[4]  Anil K. Jain,et al.  Combining classifiers for face recognition , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[5]  Sankaran Mahadevan,et al.  Vulnerability Assessment of Physical Protection Systems: A Bio-Inspired Approach , 2015, Int. J. Unconv. Comput..

[6]  Dong Chen,et al.  Novel Algorithm for Identifying and Fusing Conflicting Data in Wireless Sensor Networks , 2014, Sensors.

[7]  Xiaoyu Zhang,et al.  Interactive patent classification based on multi-classifier fusion and active learning , 2014, Neurocomputing.

[8]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[10]  Xinyang Deng,et al.  Supplier selection using AHP methodology extended by D numbers , 2014, Expert Syst. Appl..

[11]  Edmundas Kazimieras Zavadskas,et al.  Environmental impact assessment based on group decision-making methods in mining projects , 2014 .

[12]  Yong Deng,et al.  Deng entropy: a generalized Shannon entropy to measure uncertainty , 2015 .

[13]  Éloi Bossé,et al.  A new distance between two bodies of evidence , 2001, Inf. Fusion.

[14]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[15]  Sankaran Mahadevan,et al.  Reliability analysis with linguistic data: An evidential network approach , 2017, Reliab. Eng. Syst. Saf..

[16]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[17]  Yongchuan Tang,et al.  A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System , 2016, PloS one.

[18]  Akira Shimazu,et al.  Combining classifiers for word sense disambiguation based on Dempster-Shafer theory and OWA operators , 2007, Data Knowl. Eng..

[19]  Yu Luo,et al.  Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers , 2015, Int. J. Comput. Commun. Control.

[20]  Sankaran Mahadevan,et al.  Aircraft re-routing optimization and performance assessment under uncertainty , 2017, Decis. Support Syst..

[21]  Deng Yong.  A New Combination Rule of Evidence , 2006 .

[22]  Wen Jiang,et al.  A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory , 2016 .

[23]  Shi Wen-kang,et al.  Combining belief functions based on distance of evidence , 2004 .

[24]  Rolf Haenni,et al.  Are alternatives to Dempster's rule of combination real alternatives?: Comments on "About the belief function combination and the conflict management problem" - Lefevre et al , 2002, Inf. Fusion.

[25]  Néstor Becerra Yoma,et al.  ASR based pronunciation evaluation with automatically generated competing vocabulary and classifier fusion , 2009, Speech Commun..

[26]  Meng Zhao,et al.  Gabor face recognition by multi-channel classifier fusion of supervised kernel manifold learning , 2012, Neurocomputing.

[27]  Chao Fu,et al.  Robust evidential reasoning approach with unknown attribute weights , 2014, Knowl. Based Syst..

[28]  Qi Liu,et al.  Combining belief functions based on distance of evidence , 2004, Decis. Support Syst..

[29]  Xizhao Wang,et al.  Editorial: Uncertainty in learning from big data , 2015, Fuzzy Sets Syst..

[30]  Yong Deng,et al.  Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Maurice Kettner,et al.  Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory , 2015 .

[32]  Yi Yang,et al.  Multi-class SVM classifiers fusion based on evidence combination , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[33]  Xianfeng Fan,et al.  Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement , 2006, Pattern Recognit. Lett..

[34]  Gert de Cooman,et al.  A behavioral model for linguistic uncertainty , 2001, Inf. Sci..

[35]  Yong Deng,et al.  Generalized evidence theory , 2014, Applied Intelligence.

[36]  Sankaran Mahadevan,et al.  Environmental impact assessment based on D numbers , 2014, Expert Syst. Appl..

[37]  Sun,et al.  Fault Diagnosis Based on the Updating Strategy of Interval-Valued Belief Structures , 2014 .

[38]  Deng Yong.  Optimal combination of conflict evidence , 2009 .

[39]  Yong Deng.  A Threat Assessment Model under Uncertain Environment , 2015 .

[40]  José Eugenio Naranjo,et al.  Distributed Pedestrian Detection Alerts Based on Data Fusion with Accurate Localization , 2013, Sensors.

[41]  Yonghong Liu,et al.  Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations , 2013, Risk analysis : an official publication of the Society for Risk Analysis.

[42]  Eric Lefevre,et al.  Belief function combination and conflict management , 2002, Inf. Fusion.

[43]  Shuai Zhang,et al.  An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling , 2016 .

[44]  Lotfi A. Zadeh,et al.  A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..

[45]  Wen Jiang,et al.  An Improved Belief Entropy and Its Application in Decision-Making , 2017, Complex..

[46]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[47]  Henri Prade,et al.  Representation and combination of uncertainty with belief functions and possibility measures , 1988, Comput. Intell..

[48]  Catherine K. Murphy.  Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[49]  Mohamed A. Deriche,et al.  A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence , 2002, J. Artif. Intell. Res..

[50]  Weiru Liu,et al.  Analyzing the degree of conflict among belief functions , 2006, Artif. Intell..