Complex network theory-based condition recognition of electromechanical system in process industry

Science China Technological Sciences

Volumn 59, Issue 4, 2016, Pages 604-617

  Wang, RongXi ^a   Gao, JianMin ^a   Gao, ZhiYong ^a   Gao, Xu ^a   Jiang, HongQuan ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

complex electromechanical system; complex network; condition recognition; phase space reconstruction

Indexed keywords

CHEMICAL ANALYSIS; CHEMICAL PLANTS; CIRCUIT THEORY; NETWORK THEORY (GRAPHS); PHASE SPACE METHODS;

COMPLEX ELECTROMECHANICAL SYSTEMS; CONDITION RECOGNITION; DIFFERENT OPERATING CONDITIONS; DISTRIBUTED AND COMPLEX ELECTROMECHANICAL SYSTEMS; ELECTROMECHANICAL SYSTEMS; PHASE SPACE RECONSTRUCTION; STATISTICAL PROPERTIES; WEIGHTED COMPLEX NETWORKS;

COMPLEX NETWORKS;

EID: 84962449182     PISSN: 16747321     EISSN: 18691900     Source Type: Journal    
DOI: 10.1007/s11431-016-6025-2     Document Type: Article

References (30)

1. Jing C, Hou J. SVM and PCA based fault classification approaches for complicated industrial process. Neurocomputing, 2015, 167: 636–642
2. Vitanov N K, Hoffmann N P, Wernitz B. Nonlinear time series analysis of vibration data from a friction brake: SSA, PCA, and MFDFA. Chaos Soliton Fract, 2014, 69: 90–99
3. Zuo M J, Lin J, Fan X. Feature separation using ICA for a onedimensional time series and its application in fault detection. J Sound Vib, 2005, 287: 614–624
4. Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput, 1998, 10: 1299–1319
5. Vinay A, Shekhar V S, Murthy K N B, et al. Face Recognition Using Gabor Wavelet Features with PCA and KPCA–A Comparative Study. In: Soni AK, Lobiyal DK, eds. Procedia Computer Science. India: Elsevier Science. 2015, 57: 650–659
6. Bach F R, Jordan M I. Kernel independent component analysis. J Mach Learn Res, 2003, 3: 1–48
7. Kuo S C, Lin C J, Liao J R. 3D reconstruction and face recognition using kernel-based ICA and neural networks. Expert Syst Appl, 2011, 38: 5406–5415
8. Jia G F, Wu B, Hu Y M, et al. A synthetic criterion for early recognition of cutting chatter. Sci China Tech Sci, 2013, 56: 2870–2876
9. Kai S, Jianmin G, Zhiyong G, et al. Plant-wide quantitative assessment of a process industry system’s operating state based on color-spectrum. Mech Syst Signal Pr, 2015, 60: 644–655
10. Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393: 440–442
11. Barabasi A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286: 509–512
12. Boccaletti S, Latora V, Moreno Y, et al. Complex networks: Structure and dynamics. Phys Rep, 2006, 424: 175–308
13. Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks. Phys Rep, 2014, 544: 1–122
14. Zhang J, Small M. Complex network from pseudoperiodic time series: topology versus dynamics. Phys Rev Lett, 2006, 96: 238701–238701
15. Zhang J, Sun J, Luo X, et al. Characterizing pseudoperiodic time series through the complex network approach. Physica D, 2008, 237: 2856–2865
16. Mehraban S, Shirazi A H, Zamani M, et al. Coupling between time series: A network view. Europhys Lett, 2013, 103: 50011
17. Zhou T T, Tin N D, Gao Z K, et al. Limited penetrable visibility graph for establishing complex network from time series. Acta Phys Sin, 2012, 61: 030506
18. Gao Z-K, Jin N-D. A directed weighted complex network for characterizing chaotic dynamics from time series. Nonlinear Anal-Real World Appl, 2012, 13: 947–952
19. Sun X, Small M, Zhao Y, et al. Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos, 2014, 24: 297–302
20. Gao Z, Jin N. Complex network from time series based on phase space reconstruction. Chaos, 2009, 19: 375–393
21. Wu Z, Lu X, Deng Y. Image edge detection based on local dimension: A complex networks approach. Physica A, 2015, 440: 9–18
22. Gonçalves W N, Machado B B, Bruno O M. A complex network approach for dynamic texture recognition. Neurocomputing, 2015, 153: 211–220
23. Tang J, Wang Y, Wang H, et al. Dynamic analysis of traffic time series at different temporal scales: A complex networks approach. Physica A, 2014, 405: 303–315
24. Gai Y, Cai M, Shi Y. Analytical and experimental study on complex compressed air pipe network. Chin J Mech Eng-En, 2015, 28: 1023–1029
25. Allegrini P, Grigolini P, Palatella L. Intermittency and scale-free networks: a dynamical model for human language complexity. Chaos Soliton Fract, 2004, 20: 95–105
26. Tam W M, Lau F C M, Tse C K. Complex-network modeling of a call network. IEEE T Circuits-I, 2009, 56: 416–429
27. Xiao Fan W, Guanrong C. Complex networks: small-world, scalefree and beyond. IEEE Circuits Syst Mag, 2003, 3: 6–20
28. Nguyen A D, Senac P, Diaz M. Understanding and modeling the small-world phenomenon in dynamic networks. Proceedings of the ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, Paphos, the Cyprus Island, 2012. 377–384
29. Palit S K, Mukherjee S, Bhattacharya D K. A high dimensional delay selection for the reconstruction of proper phase space with cross auto-correlation. Neurocomputing, 2013, 113: 49–57
30. Lacasa L, Luque B, Ballesteros F, et al. From time series to complex networks: The visibility graph. P Natl Acad Sci Usa, 2008, 105: 4972–4975