Datum unit optimization for robustness of a journal bearing diagnosis system

International Journal of Precision Engineering and Manufacturing

Volumn 16, Issue 11, 2015, Pages 2411-2425

  Jeon, Byung Chul ^a   Jung, Joon Ha ^a   Youn, Byeng Dong ^a   Kim, Yeon Whan ^b   Bae, Yong Chae ^b  

^a Seoul National University   (South Korea)

^b Korea Electric Power Research Institute (KEPRI)   (South Korea)

Author keywords

Class separation; Datum unit; Diagnostics; Journal bearing

Indexed keywords

COMPUTER PROGRAMMING; FREQUENCY DOMAIN ANALYSIS; PLASMA DIAGNOSTICS; SEPARATION; SUPPORT VECTOR MACHINES; TIME DOMAIN ANALYSIS;

CLASS SEPARABILITY; CLASS SEPARATION; CLASSIFICATION RESULTS; DATUM UNIT; DIAGNOSIS METHODS; FISHER DISCRIMINANT; KULLBACK-LEIBLER DIVERGENCE; TIME DOMAIN FEATURES;

JOURNAL BEARINGS;

EID: 84942741407     PISSN: 22347593     EISSN: 20054602     Source Type: Journal    
DOI: 10.1007/s12541-015-0311-y     Document Type: Article

References (52)

1. Baccarini, L. M. R., Silva, V. V. R., De Menezes, B. R., and Caminhas, W. M., “SVM Practical Industrial Application for Mechanical Faults Diagnostic,” Expert Systems with Applications, Vol. 38, No. 6, pp. 6980–6984, 2011.
2. Jiang, L.-L., Yin, H.-K., Li, X.-J., and Tang, S.-W., “Fault Diagnosis of Rotating Machinery based on Multisensor Information Fusion using SVM and Time-Domain Features,” Shock and Vibration, Vol. 2014, Article ID: 418178, 2014.
3. Hu, C., Youn, B. D., Kim, T., and Wang, P., “A Co-Training-based Approach for Prediction of Remaining Useful Life Utilizing both Failure and Suspension Data,” Mechanical Systems and Signal Processing, Vols. 62-63, pp. 75–90, 2015.
4. Youn, B. D., Park, K. M., Hu, C., Yoon, J. T., Kim, H. S., et al., “Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars against Moisture Absorption,” IEEE Transactions on Energy Conversion, Vol. PP, No. 99, pp. 1–10, 2015.
5. Wang, P., Youn, B. D., Hu, C., Ha, J. M., and Jeon, B., “A Probabilistic Detectability-based Sensor Network Design Method for System Health Monitoring and Prognostics,” Journal of Intelligent Material Systems and Structures, DOI No. 10.1177/1045389X14541496, 2014.
6. Wang, P., Youn, B. D., and Hu, C., “A Generic Probabilistic Framework for Structural Health Prognostics and Uncertainty Management,” Mechanical Systems and Signal Processing, Vol. 28, pp. 622–637, 2012.
7. Hu, C., Youn, B. D., Wang, P., and Yoon, J. T., “Ensemble of Data- Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life,” Reliability Engineering & System Safety, Vol. 103, pp. 120–135, 2012.
8. Hu, C., Wang, P., Youn, B. D., Lee, W.-R., and Yoon, J. T., “Copulabased Statistical Health Grade System Against Mechanical Faults of Power Transformers,” IEEE Transactions on Power Delivery, Vol. 27, No. 4, pp. 1809–1819, 2012.
9. Gupta, K., “Vibration-A Tool for Machine Diagnostics and Condition Monitoring,” Sadhana, Vol. 22, No. 3, pp. 393–410, 1997.
10. Lin, J. and Qu, L., “Feature Extraction based on Morlet Wavelet and Its Application for Mechanical Fault Diagnosis,” Journal of Sound and Vibration, Vol. 234, No. 1, pp. 135–148, 2000.
11. Bossley, K. M., Mckendrick, R. J., Harris, C. J., and Mercer, C., “Hybrid Computed Order Tracking,” Mechanical Systems and Signal Processing, Vol. 13, No. 4, pp. 627–641, 1999.
12. Bonnardot, F., El Badaoui, M., Randall, R., Daniere, J., and Guillet, F., “Use of the Acceleration Signal of a Gearbox in order to Perform Angular Resampling (with Limited Speed Fluctuation),” Mechanical Systems and Signal Processing, Vol. 19, No. 4, pp. 766–785, 2005.
13. Villa, L. F., Reñones, A., Perán, J. R., and De Miguel, L. J., “Angular Resampling for Vibration Analysis in Wind Turbines under Non-Linear Speed Fluctuation,” Mechanical Systems and Signal Processing, Vol. 25, No. 6, pp. 2157–2168, 2011.
14. Loutridis, S. J., “Gear Failure Prediction using Multiscale Local Statistics,” Engineering Structures, Vol. 30, No. 5, pp. 1214–1223, 2008.
15. Choy, F. K., Mugler, D. H., and Zhou, J., “Damage Identification of a Gear Transmission using Vibration Signatures,” Journal of Mechanical Design, Vol. 125, No. 2, pp. 394–403, 2003.
16. Yoon, H. and Youn, B. D., “Stochastic Quantification of the Electric Power Generated by a Piezoelectric Energy Harvester using a Time- Frequency Analysis under Non-Stationary Random Vibrations,” Smart Materials and Structures, Vol. 23, No. 4, Paper No. 045035, 2014.
17. He, Q., Yan, R., Kong, F., and Du, R., “Machine Condition Monitoring using Principal Component Representations,” Mechanical Systems and Signal Processing, Vol. 23, No. 2, pp. 446–466, 2009.
18. Xi, Z., Youn, B. D., and Hu, C., “Random Field Characterization Considering Statistical Dependence for Probability Analysis and Design,” Journal of Mechanical Design, Vol. 132, No. 10, Paper No. 101008, 2010.
19. He, Q., Du, R., and Kong, F., “Phase Space Feature based on Independent Component Analysis for Machine Health Diagnosis,” Journal of Vibration and Acoustics, Vol. 134, No. 2, Paper No. 021014, 2012.
20. Lei, Y., Lin, J., He, Z., and Zuo, M. J., “A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery,” Mechanical Systems and Signal Processing, Vol. 35, No. 1, pp. 108–126, 2013.
21. Zhao, X., Patel, T. H., and Zuo, M. J., “Multivariate Emd and Full Spectrum based Condition Monitoring for Rotating Machinery,” Mechanical Systems and Signal Processing, Vol. 27, pp. 712–728, 2012.
22. Wang, D., Miao, Q., and Kang, R., “Robust Health Evaluation of Gearbox Subject to Tooth Failure with Wavelet Decomposition,” Journal of Sound and Vibration, Vol. 324, No. 3, pp. 1141–1157, 2009.
23. Sanz, J., Perera, R., and Huerta, C., “Fault Diagnosis of Rotating Machinery based on Auto-Associative Neural Networks and Wavelet Transforms,” Journal of Sound and Vibration, Vol. 302, No. 4, pp. 981–999, 2007.
24. Liu, B., “Selection of Wavelet Packet Basis for Rotating Machinery Fault Diagnosis,” Journal of Sound and Vibration, Vol. 284, No. 3, pp. 567–582, 2005.
25. Jeon, B., Jung, J., Youn, B. D., Kim, Y., and Bae, Y.-C., “Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System using Fisher Discriminant Analysis,” Proc. of European Conference of the Prognostics and Health Management Society, 2014.
26. Han, T., Yang, B.-S., Choi, W.-H., and Kim, J.-S., “Fault Diagnosis System of Induction Motors based on Neural Network and Genetic Algorithm using Stator Current Signals,” International Journal of Rotating Machinery, Vol. 2006, Article ID: 61690, 2006.
27. Yang, B.-S. and Kim, K. J., “Application of Dempster-Shafer Theory in Fault Diagnosis of Induction Motors using Vibration and Current Signals,” Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 403–420, 2006.
28. Neves, W. L. A., Brito, N. S. D., Souza, B. A., Fontes, A. V., Dantas, K. M. C., et al., “Sampling Rate of Digital Fault Recorders Influence on Fault Diagnosis,” Proc. of IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, pp. 406–411, 2004.
29. Zhang, P. and Ding, S. X., “Influence of Sampling Period on a Class of Optimal Fault-Detection Performance,” IEEE Transactions on Automatic Control, Vol. 54, No. 6, pp. 1396–1402, 2009.
30. Kullback, S. and Leibler, R. A., “On Information and Sufficiency,” The Annals of Mathematical Statistics, pp. 79–86, 1951.
31. Theodoridis, S. and Koutroumbas, K., “Pattern Recognition,” Academic Press, 4th Ed., pp. 280–283, 2008.
32. Roush, M. L. and Webb, W. M., “Applied Reliability Engineering,” Center for Reliability Engineering, University of Maryland, pp. 90–93, 2006.
33. Kankar, P. K., Sharma, S. C., and Harsha, S., “Fault Diagnosis of Ball Bearings using Continuous Wavelet Transform,” Applied Soft Computing, Vol. 11, No. 2, pp. 2300–2312, 2011.
34. Abbasion, S., Rafsanjani, A., Farshidianfar, A., and Irani, N., “Rolling Element Bearings Multi-Fault Classification based on the Wavelet Denoising and Support Vector Machine,” Mechanical Systems and Signal Processing, Vol. 21, No. 7, pp. 2933–2945, 2007.
35. Widodo, A. and Yang, B.-S., “Support Vector Machine in Machine Condition Monitoring and Fault Diagnosis,” Mechanical Systems and Signal Processing, Vol. 21, No. 6, pp. 2560–2574, 2007.
36. Lei, Y., He, Z., and Zi, Y., “Application of an Intelligent Classification Method to Mechanical Fault Diagnosis,” Expert Systems with Applications, Vol. 36, No. 6, pp. 9941–9948, 2009.
37. Lin, J. and Zuo, M., “Gearbox Fault Diagnosis Using Adaptive Wavelet Filter,” Mechanical Systems and Signal Processing, Vol. 17, No. 6, pp. 1259–1269, 2003.
38. Sreejith, E., Verma, A., and Srividya, A., “Fault Diagnosis of Rolling Element Bearing using Time-Domain Features and Neural Networks,” Proc. of IEEE Region 10 and the 3rd international Conference on Industrial and Information Systems, pp. 1–6, 2008.
39. Sun, H.-C., Huang, C.-M., and Huang, Y.-C., “Fault Diagnosis of Steam Turbine-Generator Sets using an EPSO-based Support Vector Classifier,” IEEE Transactions on Energy Conversion, Vol. 28, No. 1, pp. 164–171, 2013.
40. ISO 7919-2, “Mechanical Vibration- Evaluation of Machine Vibration by Measurements on Rotating Shafts- Part 2: Land-based Steam Turbines and Generators in Excess of 50 MW with Normal Operating Speeds of 1500 r/min, 1800 r/min, 3000 r/min and 3600 r/min,” 2001.
41. Eisenmann, R. C., “Machinery Malfunction Diagnosis and Correction: Vibration Analysis and Troubleshooting for Process Industries,” Prentice Hall PTR, 1st Ed., pp. 310–312, 1997.
42. Guo, B., Damper, R. I., Gunn, S. R., and Nelson, J. D., “A Fast Separability-based Feature-Selection Method for High-Dimensional Remotely Sensed Image Classification,” Pattern Recognition, Vol. 41, No. 5, pp. 1653–1662, 2008.
43. Afgani, M., Sinanoviæ, S., and Haas, H., “Anomaly Detection using the Kullback-Leibler Divergence Metric,” Proc. of IEEE 1st International Symposium on Applied Sciences on Biomedical and Communication Technologies, pp. 1–5, 2008.
44. Johnson, D. H., Gruner, C. M., Baggerly, K., and Seshagiri, C., “Information-Theoretic Analysis of Neural Coding,” Journal of Computational Neuroscience, Vol. 10, No. 1, pp. 47–69, 2001.
45. Rao, S. S., “Reliability-based Design,” Mcgraw-Hill, pp. 237–240, 1992.
46. Ludwig, O., Nunes, U., Araújo, R., Schnitman, L., and Lepikson, H. A., “Applications of Information Theory, Genetic Algorithms, and Neural Models to Predict Oil Flow,” Communications in Nonlinear Science and Numerical Simulation, Vol. 14, No. 7, pp. 2870–2885, 2009.
47. Ludwig, O. and Nunes, U., “Novel Maximum-Margin Training Algorithms for Supervised Neural Networks,” IEEE Transactions on Neural Networks, Vol. 21, No. 6, pp. 972–984, 2010.
48. Eshelman, L. J. and Schaffer, J. D., “Real-Coded Genetic Algorithms and Interval-Schemata,” Foundations of Genetic Algorithms, pp. 187–202, 1992.
49. Hsu, C.-W. and Lin, C.-J., “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415–425, 2002.
50. Chang, C.-C. and Lin, C.-J., “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 2, No. 3, Article No. 27, 2011.
51. Lei, Y., He, Z., and Zi, Y., “Application of the Eemd Method to Rotor Fault Diagnosis of Rotating Machinery,” Mechanical Systems and Signal Processing, Vol. 23, No. 4, pp. 1327–1338, 2009.
52. Friswell, M. I., “Dynamics of Rotating Machines,” Cambridge University Press, pp. 61–64, 2010.