Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

Mechanical Systems and Signal Processing

Volumn 66-67, Issue , 2016, Pages 699-714

  Ruiz Carcel C ^a   Jaramillo, V H ^b   Mba, D ^c   Ottewill, J R ^b   Cao, Y ^a  

^a CRANFIELD UNIVERSITY   (United Kingdom)

^b ABB CORPORATE RESEARCH CENTER   (Poland)

^c LONDON SOUTH BANK UNIVERSITY   (United Kingdom)

Author keywords

Canonical Variate Analysis; Compressor; Condition monitoring; Non stationary operation; Vibration

Indexed keywords

COMPRESSORS; CONDITION MONITORING; FAULT DETECTION; INDUSTRIAL RESEARCH; LEAST SQUARES APPROXIMATIONS; MACHINERY; MONITORING; PRINCIPAL COMPONENT ANALYSIS; PROCESS MONITORING; VIBRATIONS (MECHANICAL);

CANONICAL VARIATE ANALYSIS; DETECTION AND DIAGNOSIS; MONITORING TECHNIQUES; NONSTATIONARY; PARTIAL LEAST SQUARE (PLS); STEADY-STATE LOADINGS; VARIABLE OPERATING CONDITION; VIBRATION;

VIBRATION ANALYSIS;

EID: 84946474124     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.05.018     Document Type: Article

References (45)

1. L.H. Chiang, E.L. Rusell, and R.D. Braatz Fault Detection and Diagnosis in Industrial Systems 1st ed. 2000 Springer London, UK
2. B.C. Juricek, D.E. Seborg, and W.E. Larimore Fault detection using canonical variate analysis Ind. Eng. Chem. Res. 43 2 2004 458 474
3. A. Simoglou, E.B. Martin, and A.J. Morris Statistical performance monitoring of dynamic multivariate processes using state space modelling Comput. Chem. Eng. 26 6 2002 909 920
4. M.Y.M. Yunus, J. Zhang, Multivariate process monitoring using classical multidimensional scaling and procrustes analysis, IFAC Proceedings Volumes (IFAC - Papers Online), vol. 9, 2010, pp. 165.
5. K. Zhou, Q. Li, R. Guo, Improving monitoring accuracy of process based on SPC method, in: Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012, 2012, p. 1488.
6. A. Alkaya, and ̄. Eker Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application ISA Trans. 50 2 2011 287 302
7. H.J. Borsje, Fault detection in boilers using canonical variate analysis, in: Proceedings of the American Control Conference, vol. 2, 1999, p. 1167.
8. P. Eserin, Application of canonical variate analysis to the dynamical modeling and control of drum level in an industrial boiler, in: Proceedings of the American Control Conference, vol. 2, 1999, p. 1163.
9. H.W. Lee, M.W. Lee, and J.M. Park Multi-scale extension of PLS algorithm for advanced on-line process monitoring Chemom. Intell. Lab. Syst. 98 2 2009 201 212
10. K. Peng, G. Li, and K. Zhang Strip thickness monitoring in hot strip mill processes based on dynamic total projection to latent structures (T-PLS) algorithm Kongzhi Lilun Yu Yingyong/Control Theory Appl. 29 11 2012 1446 1451
11. E. Vanhatalo Multivariate process monitoring of an experimental blast furnace Qual. Reliab. Eng. Int. 26 5 2010 495 508
12. Y. Rotem, A. Wachs, and D.R. Lewin Ethylene compressor monitoring using model-based PCA AIChE J. 46 9 2000 1825 1836
13. T. Komulainen, M. Sourander, and S. Jämsä-Jounela An online application of dynamic PLS to a dearomatization process Comput. Chem. Eng. 28 12 2004 2611 2619
14. W. Ku, R.H. Storer, and C. Georgakis Disturbance detection and isolation by dynamic principal component analysis Chemom. Intell. Lab. Syst. 30 1 1995 179 196
15. E.L. Russell, L.H. Chiang, and R.D. Braatz Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis Chemom. Intell. Lab. Syst. 51 1 2000 81 93
16. P.P. Odiowei, and Y. Cao Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations Comput. Aided Chem. Eng. 27 C 2009 1557 1562
17. C.J. Stander, P.S. Heyns, and W. Schoombie Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions Mech. Syst. Signal Process. 16 6 2002 1005 1024
18. P.D. McFadden Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration J. Vib. Acoust. Stress Reliab. Des. 108 2 1986 165 170
19. P.D. McFadden Determining the location of a fatigue crack in a gear from the phase of the change in the meshing vibration Mech. Syst. Signal Process. 2 4 1988 403 409
20. G. Dalpiaz, A. Rivola, and R. Rubini Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears Mech. Syst. Signal Process. 14 3 2000 387 412
21. N. Baydar, and A. Ball A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution Mech. Syst. Signal Process. 15 6 2001 1091 1107
22. B.E. Parker Jr., H.A. Ware, D.P. Wipf, W.R. Tompkins, B.R. Clark, E.C. Larson, and H.V. Poor Fault diagnostics using statistical change detection in the bispectral domain Mech. Syst. Signal Process. 14 4 2000 561 570
23. Y. Zhan, V. Makis, and A.K.S. Jardine Adaptive state detection of gearboxes under varying load conditions based on parametric modelling Mech. Syst. Signal Process. 20 1 2006 188 221
24. S. Braun The synchronous (time domain) average revisited Mech. Syst. Signal Process. 25 4 2011 1087 1102
25. W. Bartelmus, and R. Zimroz A new feature for monitoring the condition of gearboxes in non-stationary operating conditions Mech. Syst. Signal Process. 23 5 2009 1528 1534
26. R. Zimroz, W. Bartelmus, T. Barszcz, and J. Urbanek Diagnostics of bearings in presence of strong operating conditions non-stationarity - a procedure of load-dependent features processing with application to wind turbine bearings Mech. Syst. Signal Process. 46 1 2014 16 27
27. H. Hotelling Relations between two sets of variates Biometrika 28 3-4 1936 321 377
28. H. Akaike Markovian representation of stochastic processes by canonical variables. SIAM J. Control 13 1 1975 162 173
29. W.E. Larimore, System identification, reduced-order filtering and modeling via canonical variate analysis, in: Proceedings of the American Control Conference, vol. 2, 1983, p. 445.
30. X. Deng, X. Tian, A new fault isolation method based on unified contribution plots, in: Proceedings of the 30th Chinese Control Conference, CCC 2011, 2011, p. 4280.
31. J. Liu, and D. Chen Multiple sensor fault isolation using contribution plots without smearing effect to non-faulty variables Comput. Aided Chem. Eng. 31 2012 1517 1521
32. B. Mnassri, E.M. El Adel, B. Ananou, M. Ouladsine, Fault detection and diagnosis based on PCA and a new contribution plots, IFAC Proceedings Volumes (IFAC - Papers Online), 2009, p. 834.
33. A.W. Ramírez, J.C. Llinàs, Fault diagnosis of batch processes release using PCA contribution plots as fault signatures, in: ICEIS 2011 - Proceedings of the 13th International Conference on Enterprise Information Systems, vol. 1 DISI, 2011, p. 223.
34. P. Pennacchi, and A. Vania Diagnostics of a crack in a load coupling of a gas turbine using the machine model and the analysis of the shaft vibrations Mech. Syst. Signal Process. 22 5 2008 1157 1178
35. M. Ahmed, M. Baqqar, F. Gu, A.D. Ball, Fault detection and diagnosis using principal component analysis of vibration data from a reciprocating compressor, in: Proceedings of the 2012 UKACC International Conference on Control, CONTROL 2012, 2012, p. 461.
36. L. Cui, J. Wang, and S. Lee Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis J. Sound Vib. 333 10 2014 2840 2862
37. A.S. Sekhar, and B.S. Prabhu Effects of coupling misalignment on vibrations of rotating machinery J. Sound Vib. 185 4 1995 655 671
38. T.H. Patel, and A.K. Darpe Vibration response of misaligned rotors J. Sound Vib. 325 3 2009 609 628
39. R.D. Avendano, and D.W. Childs One explanation for two-times running speed response due to misalignment in rotors connected by flexible couplings J. Eng. Gas Turbines Power 135 6 2013
40. P.N. Saavedra, and D.E. Ramírez Vibration analysis of rotors for the identification of shaft misalignment. Part 1: theoretical analysis Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 218 9 2004 971 985
41. P.N. Saavedra, and D.E. Ramírez Vibration analysis of rotors for the identification of shaft misalignment. Part 2: experimental validation Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci. 218 9 2004 987 999
42. C.B. Gibbons, Coupling missalignment forces, in: Proceedings of the 5th Turbomach Symposium, October 1976, Texas, USA, p. 111.
43. Z.K. Peng, F.L. Chu, and P.W. Tse Singularity analysis of the vibration signals by means of wavelet modulus maximal method Mech. Syst. Signal Process. 21 2 2007 780 794
44. A.S. Sekhar Identification of unbalance and crack acting simultaneously in a rotor system: modal expansion versus reduced basis dynamic expansion J. Vib. Control 11 9 2005 1125 1145
45. A. Negiz, and A. Çinar Monitoring of multivariable dynamic processes and sensor auditing J. Process Control 8 5-6 1998 375 380