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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
Zhou DH, Hu YY (2009) Fault diagnosis techniques for dynamic systems. Acta Autom Sinica 35(6):748–758 (in Chinese)
Lemos A, Caminhas W, Gomide F (2013) Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Inf Sci 220:64–85
Ghate VN, Dudul SV (2011) Cascade neural-network-based fault classifier for three-phase induction motor. IEEE Trans Ind Electron 58(5):1555–1563
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)
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
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
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
Zhang QH (2004) Fault diagnosis technology research based on artificial immune system. South China University of Technology, Guangzhou (in Chinese)
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
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
Jin ZZ, Liao MH, Xiao G (2013) Survey of negative selection algorithms. J Commun 34(1):159–170 (in Chinese)
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)
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
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)
Zheng YH, Li RH (2010) Improved fault diagnosis algorithm based on immune network model. Control Decis 25(6):847–851 (in Chinese)
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
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
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
Wang C, Zhao Y (2008) A new fault detection method based on artificial immune systems. Asia-Pac J Chem Eng 3(6):706–711
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
Acknowledgments
This work was financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201404081).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-662-46463-2_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46462-5
Online ISBN: 978-3-662-46463-2
eBook Packages: EngineeringEngineering (R0)