Abstract
Multimedia technologies are increasingly used in design of CNC grinding machines. The self-diagnosis system is a necessary part. To make use of multimedia and solve the problem about the lack of diagnosis knowledge included in the self-diagnosis module used in CNC grinding machines, this paper addresses the fault diagnosis knowledge discovery and the decision support model for fault diagnosis designed in a CNC grinding machine based on on-line vibration monitoring. The fault diagnosis characteristics of CNC grinding machine are analyzed first. Then the rough set theory is introduced to reduce the redundant information affecting fault diagnosis, thereby discover the fault diagnosis knowledge in the form of rules. Further, this paper proposes a decision support model of fault diagnosis module for modern CNC grinding machine, based on the theory advantages of redundant information reduction supported by rough set, being easy to find the diagnosis knowledge in the maintenance and fault diagnosis records. The model is applied into an engineering example of fault diagnosis for a given type of grinding machine. Useful knowledge of fault diagnosis is discovered with the engineering interpretation of fault diagnosis process. The performance comparison with other typical diagnosis methods verifies the validity and advantage of the proposed decision support model based on rough set theory. This paper provides a multimedia solution integrated with rough set theory and information technology for diagnosis module developed in modern CNC grinding machines, and can be a theoretical and technical reference to build fault diagnosis systems used in other modern machines.
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References
Daigle MJ et al (2010) A comprehensive diagnosis methodology for complex hybrid systems: a case study on spacecraft power distribution systems. IEEE Trans Syst Man Cybern Part a-Syst Hum 40(5):917–931
Feng Z et al (2015) Time-frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions. Mech Syst Signal Process 62–63:54–74
Huang C, Liang WY, Tseng TL, Wong RY (2015) A rough set-based corporate memory for the case of ecotourism. Tour Manag 47:22–33
Jiang S (2015) Y. F. A novel approach for discretization of continuous attributes in rough set theory. Knowl-Based Syst 73:324–334
Kamel T et al (2015) Fault diagnosis and on-line monitoring for grid-connected single-phase inverters. Electr Power Syst Res 126:68–77
Keliris C et al (2015) Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems. Int J Control 88(8):1472–1489
Krysander M et al (2008) An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis. IEEE Trans Syst Man Cybern Part a-Syst Hum 38(1):197–206
Lazo-cortes MS, Martimnez-trinidad JF, Carrasco-ochoa JA, Sanchez-diaz G (2015) On the relation between rough set reducts and typical testors. Inf Sci 294:152–163
Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostics tools and E-maintenance. Comput Ind 57(6):476–489
Li H, Xiao D (2015) Fault diagnosis based on power spectral density basis transform. J Vib Control 21(12):2416–2433
Liu Q (2001) Rough set and rough reasoning. Science Press, Bejing
Mafarja M, Abdullah S (2015) A fuzzy record-to-record travel algorithm for solving rough set attribute reduction. Int J Syst Sci 46:503–512
Montes DOS et al (2012) Fault-tolerant control strategy for actuator faults using LPV techniques: application to a two degree of freedom helicopter. Int J Appl Math Comput Sci 22(1):161–171
Moosavi SS et al (2015) Demagnetization fault diagnosis in permanent magnet synchronous motors: a review of the state-of-the-art. J Magn Magn Mater 391:203–212
Wang T et al (2015) Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification. Mech Syst Signal Process 62–63:30–53
Xia X et al (2015) A novel method for fault diagnosis of hydro generator based on NOFRFs. Int J Electr Power Energy Syst 71:60–67
Zhao RY (2005) Research on rough set theory, the digital factory technology and application in enterprise production DSS(Chinese) 2005;Shanghai: Tongji University
Zhao RY et al (2006) A new machine learning method based on PCA and SVM. 2006 International Conference on Computational Intelligence and Security, 3–6 Nov. 2006, Piscataway, NJ, USA, IEEE
Zheng Z et al (2015) Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mech Mach Theory 91:151–167
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 71373178) and Natural Science Foundation of Shanghai, China (Grant No. 13ZR1444700 and 15ZR1420100).
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Zhao, R., Li, C. & Tian, X. A novel industrial multimedia: rough set based fault diagnosis system used in CNC grinding machine. Multimed Tools Appl 76, 19913–19926 (2017). https://doi.org/10.1007/s11042-016-3878-0
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DOI: https://doi.org/10.1007/s11042-016-3878-0