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Artificial Immune System Used in Rotating Machinery Fault Diagnosis

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 337))

Abstract

The concept and application of artificial immune system (AIS) is summarized together with the introduction of common Fault Diagnosis in Rotation Machinery. By analysis, the current status of artificial immune system which used in rotation machinery, comments for the achievements and challenge questions are given out in this paper. Furthermore, the expectation and prospect about AIS used in fault diagnosis are discussed also in conclusion.

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References

  1. Wei D, Zhan SL, Xiao WW (2007) Application of image recognition based on artificial immune in rotating machinery fault diagnosis. In: 1st international conference on bioinformatics and biomedical engineering, pp 1047–1052

    Google Scholar 

  2. Zhou DH, Hu YY (2009) Fault diagnosis techniques for dynamic systems. Acta Autom Sinica 35(6):748–758 (in Chinese)

    Article  Google Scholar 

  3. Lemos A, Caminhas W, Gomide F (2013) Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Inf Sci 220:64–85

    Article  Google Scholar 

  4. Ghate VN, Dudul SV (2011) Cascade neural-network-based fault classifier for three-phase induction motor. IEEE Trans Ind Electron 58(5):1555–1563

    Article  Google Scholar 

  5. Zhou DH, Shi JT, He X (2014) Review of intermittent fault diagnosis techniques for dynamic systems. Acta Autom Sinica 40(2):161–171 (in Chinese)

    Google Scholar 

  6. Chang J, Li T, Li P (2010) The selection of time domain characteristic parameters of rotating machinery fault diagnosis. In: 2010 international conference on logistics systems and intelligent management. IEEE, vol 1, pp 619–623

    Google Scholar 

  7. Li P, Kong F, He Q et al (2013) Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis. Measurement 46(1):497–505

    Article  Google Scholar 

  8. Lima AA, Prego MT, Netto SL et al (2013) On fault classification in rotating machines using fourier domain features and neural networks. In: IEEE fourth latin american symposium on circuits and systems (LASCAS), IEEE, 2013, pp 1–4

    Google Scholar 

  9. Zhang QH (2004) Fault diagnosis technology research based on artificial immune system. South China University of Technology, Guangzhou (in Chinese)

    Google Scholar 

  10. Wei D, Zhan SL (2009) A recognition method of vibration parameter image based on improved immune negative selection algorithm for rotating machinery. J Harbin Inst Technol 16(1):5–10

    Google Scholar 

  11. Duan F, Lei M, Li J et al (2007) A motor fault diagnosis method based on immune mechanism. In: Workshop on intelligent information technology application, IEEE, pp 157–160

    Google Scholar 

  12. Hou SL, Li YH, Li MK, Wei XK (2007) Sensor fault diagnosis for aircraft engine based on artificial immune networks. J Propul Technol 28(1):86–91 (in Chinese)

    Google Scholar 

  13. Abbass HA, Newton CS, Sarker R (2002) Data Mining: A Heuristic Approach. IGI Global, Hershey, PA, pp 1–310. doi:10.4018/978-1-93070-825-9

  14. Immovilli F, Bellini A, Rubini R et al (2010) Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans Ind Appl 46(4):1350–1359

    Article  Google Scholar 

  15. Wen X, Wei S, Liu H et al (2008) Application research of immune neural network on motor fault diagnosis. In: International workshop on education technology and training and 2008 international workshop on geoscience and remote sensing ETT and GRS. IEEE, (1), 618–621

    Google Scholar 

  16. Zhang P, Du Y, Habetler TG et al (2011) A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl 47(1):34–46

    Article  Google Scholar 

  17. Chilengue Z, Dente JA, Branco PJ (2011) An artificial immune system approach for fault detection in the stator and rotor circuits of induction machines. Electr Power Syst Res 81(1):158–169

    Article  Google Scholar 

  18. Sun G, Qin A, Zhang Q et al (2013) A compound fault integrated diagnosis method for rotating machinery base on dimensionless immune detector. In: 25th Chinese control and decision conference (CCDC). IEEE, 2013, pp 4390–4394

    Google Scholar 

  19. Zhang CL, Yue X, Li S et al (2011) Fault diagnosis of rotating machinery base on wavelet packet energy moment and HMM. Key Eng Mater 455:558–564

    Article  Google Scholar 

  20. Zhang QH, Hu Q, Sun G et al (2013) Concurrent fault diagnosis for rotating machinery based on vibration sensors. Int J Distrib Sens Netw 2013:1–10

    Google Scholar 

  21. Tian Y (2009) On diagnosis prototype system for motor faults based on immune model. In: International conference on business intelligence and financial engineering, 2009. BIFE’09. IEEE, pp 126–129

    Google Scholar 

  22. Ma X, Wei X, An F et al (2010) Bearing fault diagnosis based on negative selection algorithm of feature extraction and neural network. In: Control and decision conference (CCDC), Chinese. IEEE, 2010, pp 3938–3941

    Google Scholar 

  23. Tang P, Gan Z, Chow TWS (2011) Clonal selection programming for rotational machine fault classification and diagnosis. In: Prognostics and system health management conference (PHM-Shenzhen). IEEE, 2011, pp 1–6

    Google Scholar 

  24. Wen X, Brown DJ (2010) Online motor fault diagnosis using hybrid intelligence techniques. In: IEEE symposium on industrial electronics & applications (ISIEA). IEEE, 2010, pp 355–360

    Google Scholar 

  25. Jin ZZ, Liao MH, Xiao G (2013) Survey of negative selection algorithms. J Commun 34(1):159–170 (in Chinese)

    Google Scholar 

  26. Liu Y, Shang YS, Wang YP (2011) Fault diagnosis method based on immune model and its application. Comput Eng 37(16):5–7 (in Chinese)

    MATH  Google Scholar 

  27. Govender P, Kyereahene, Mensah DA (2010). Fault diagnosis based on the artificial immune algorithm and negative selection. In: 2010 IEEE 17th international conference on industrial engineering and engineering management (IE&EM). IEEE, pp 418–423

    Google Scholar 

  28. Gui LY, Shi WQ, Jian Z (2012) Fault diagnosis of induction motor based on artificial immune system. In: International conference on industrial control and electronics engineering (ICICEE)

    Google Scholar 

  29. Zheng YH, Li RH (2010) Improved fault diagnosis algorithm based on immune network model. Control Decis 25(6):847–851 (in Chinese)

    MathSciNet  Google Scholar 

  30. Cen J, Zhang QH, Xu BG et al (2009) Fault diagnosis model of rotating machinery based on artificial immunity and its application. In: International workshop on intelligent systems and applications. ISA 2009. IEEE, 2009, pp 1–4

    Google Scholar 

  31. Alizadeh E, Meskin N, Benammar M et al (2013) Fault detection and isolation of the wind turbine based on the real-valued negative selection algorithm. In: 2013 7th IEEE GCC conference and exhibition (GCC). IEEE, pp 11–16

    Google Scholar 

  32. Gao XZ, Wang X, Zenger K et al (2012) Negative selection algorithm-based motor fault diagnosis. In: Practical applications of intelligent systems. Springer, Berlin, Heidelberg, pp 173–183

    Google Scholar 

  33. Wang C, Zhao Y (2008) A new fault detection method based on artificial immune systems. Asia-Pac J Chem Eng 3(6):706–711

    Article  Google Scholar 

  34. Aydin I, Karakose M, Akin E (2010) Generation of classification rules using artificial immune system for fault diagnosis. In: 2010 IEEE international conference on systems man and cybernetics (SMC), IEEE, pp 343–349

    Google Scholar 

Download references

Acknowledgments

This work was financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201404081).

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Correspondence to Yaping Dai .

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Zhao, L., Zhou, L., Dai, Y., Dai, Z. (2015). Artificial Immune System Used in Rotating Machinery Fault Diagnosis. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46463-2_8

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  • DOI: https://doi.org/10.1007/978-3-662-46463-2_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46462-5

  • Online ISBN: 978-3-662-46463-2

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