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Fault-Diagnosis of Machine Assembly Based on Fuzzy ES

Chen, J.; Zhang, K.R.; Zhu, W.G.

Advanced Materials Research 490-495: 343-347

2012

ISSN/ISBN: 1662-8985
DOI: 10.4028/www.scientific.net/amr.490-495.343
Accession: 084002981

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Summary
Based on the working flow of an automated machine assembly line, a fuzzy expert system (FES) for fault diagnosis of the assembly line is developed, which contains user interface, knowledge base, inference engine and history database. The FES adopts fuzzy forward chaining rule-based reference strategy and can predict the severity ponderance of faults according to certainty factor. In addition, fuzzy logic and linguistic terms are also introduced.

References

Wu, Q. 2011: Hybrid fuzzy support vector classifier machine and modified genetic algorithm for automatic car assembly fault diagnosis Expert Systems with Applications 38(3): 1457-1463
Wu, Q.; Law, R.; Wu, S. 2011: Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm Expert Systems with Applications 38(8): 9096-9104
Wu, Q.; Ni, Z. 2011: Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm Expert Systems with Applications 38(5): 4734-4740
Wu, Q. 2011: Car assembly line fault diagnosis model based on triangular fuzzy Gaussian wavelet kernel support vector classifier machine and genetic algorithm Expert Systems with Applications 38(12): 14812-14818
Wong, P.K.; Vong, C.; Zhang, Z.; Xu, Q. 2011: Fault diagnosis of automotive engines using fuzzy relevance vector machine Communications in Computer and Information Science 164 CCIS: 213-220
Lu, N.; Xu, B.; Chen, Z.; Ren, D.; Yang, Y.; Bian, P. 2020: Simulation Research on Fault Diagnosis of CNC Machine Tools Based on Fuzzy Petri Net Lecture Notes in Electrical Engineering 593: 41-49
Sheng, Z.H.A.N.G.; Asakura, T.; Xiaoli, X.U.; Baojie, X.U. 2003: Fault diagnosis system for rotary machine based on fuzzy neural networks Jsme International Journal. Series C, Mechanical Systems, Machine Elements and Manufacturing 46(3): 1035-1041
Wang, P.; Han, H.; Shan, F.; Huang, X.; Wang, Y.; Wu, M.; Zhao, X.; Wang, L. 2022: An Equipment Fault Diagnosis Method based on Fuzzy Support Vector Machine Proceedings of SPIE - The International Society for Optical Engineering 12348: 123480F
Dong, X.; Tao, J.; Wang, H.; Liu, F. 2010: Incremental algorithm of adaptive fuzzy support vector machine in transformer fault diagnosis Dianli Zidonghua Shebei/Electric Power Automation Equipment 30(11): 48-52
TayebiHaghighi, S.; Koo, I. 2022: Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm Sensors 22(8)
Jayaswal, P.; Verma, S.; Wadhwani, A. 2010: Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis Journal of Quality in Maintenance Engineering 16(2): 190-213
Wei, W.; Liu, X.; Zhang, X.; Wu, X. 2022: Fault Diagnosis of S700K Switch Machine Based on EEMD Multi-scale Fuzzy Entropy Tiedao Xuebao/Journal of the China Railway Society 44(5): 60-66
Yong MAO; Zheng XIA; Zheng YIN; Youxian SUN; Zheng WAN 2007: Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection Chinese Journal of Chemical Engineering 15(2): 233-239
Wu, Q. 2010: Car assembly line fault diagnosis based on modified support vector classifier machine Expert Systems with Applications 37(9): 6352-6358
Hang, J.; Zhang, J.; Cheng, M. 2016: Application of multi-class fuzzy support vector machine classifier for fault diagnosis of wind turbine Fuzzy Sets and Systems 297: 128-140
Jeong, K.; Choi, S.B. 2021: Takagi-Sugeno Fuzzy Observer-Based Magnetorheological Damper Fault Diagnosis Using a Support Vector Machine IEEE Transactions on Control Systems Technology: 1-13
Wu, Q. E.; Guang, M.; Chen, H.; Sun, L. 2020: Semigroup of fuzzy automata and its application for fast accurate fault diagnosis on machine and anti-fatigue control Applied Intelligence 50(5): 1542-1557
Bastani, K.; Kong, Z.; Huang, W.; Huo, X.; Zhou, Y. 2013: Fault Diagnosis Using an Enhanced Relevance Vector Machine (RVM) for Partially Diagnosable Multistation Assembly Processes IEEE Transactions on Automation Science and Engineering 10(1): 124-136
Zhu, K.; Li, H. 2015: A rolling element bearing fault diagnosis approach based on hierarchical fuzzy entropy and support vector machine Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230(13): 2314-2322
Wang, Z.; Zhao, N.; Wang, W.; Tang, R.; Li, S. 2015: A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine Mathematical Problems in Engineering 2015: 1-11