Fuzzy Identification of The Reliability State of The Mine Detecting Ship Propulsion System

1. Antoni J.: Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration 2007 (304), pp. 497–529.10.1016/j.jsv.2007.02.029Search in Google Scholar

2. Bianchi D., Mayrhofer E., Groeschl M., Betz G., Vernes A.: Wavelet packet transform for detection of single events in acoustic emission signals. Mechanical Systems and Signal Processing 2015, Vol. 64-65, p. 441–451.10.1016/j.ymssp.2015.04.014Search in Google Scholar

3. Bielawski, P.: Diagnostics of marine propeller shafts. Journal of Polish CIMAC, 2011 Vol. 6 No. 2, pp. 31-40.Search in Google Scholar

4. Borghesani P., Pennacchi P., Chatterton S., Ricci R.: The velocity synchronous discrete Fourier transform for order tracking in the field of rotating machinery. Mechanical Systems and Signal Processing 2014, 44, pp. 118-133.10.1016/j.ymssp.2013.03.026Search in Google Scholar

5. Castejon C., Gomez M., Garcia-Prada J., Ordonez A., Rubio H.: Automatic Selection of the WPT Decomposition Level for Condition Monitoring of Rotor Elements Based on the Sensitivity Analysis of the Wavelet Energy. International Journal of Acoustic Vibration 2015, Vol. 20, Issue 2, pp. 95–100.Search in Google Scholar

6. Coats M.D., Randall R.B.: Single and multi-stage phase demodulation based order-tracking. Mechanical Systems and Signal Processing 2014, 44, pp. 86–117.10.1016/j.ymssp.2013.09.016Search in Google Scholar

7. Gang Tang, QinYang, Hua-Qing Wang, Gang-gang Luo, Jianwei Ma: Sparse classification of rotating machinery faults based on compressive sensing strategy. Mechatronics 2015, Volume 31, pp. 60–67.10.1016/j.mechatronics.2015.04.006Search in Google Scholar

8. Girtler J.: The semi-Markov model of the process of appearance of sea-going ship propulsion system ability and inability states in application to determining the reliability of these systems. Polish Maritime Research 4(80) 2013, Vol 20, pp. 18–24.10.2478/pomr-2013-0036Search in Google Scholar

9. Górniewicz I., Roman S.: Mathematical Analysis for Physicians. Scientific. Publishing Office of Nicolaus Copernicus University of Torun, Torun 2000.Search in Google Scholar

10. Grządziela A., Musiał J., Muślewski Ł., Pająk M.: A method for identification of non-coaxiality in engine shaft lines of a selected type of naval ships. Polish Maritime Research 2015, 1(85) Vol. 22, pp. 65–71.10.1515/pomr-2015-0009Search in Google Scholar

11. Grządziela A. Muślewski Ł.: High quality simulation of the effects of underwater detonation impact. Journal of Vibroengineering, 2013, Issue 1, Volume 15.Search in Google Scholar

12. Gurr C., Rulfs H.: Influence of transient operating conditions on propeller shaft bearings. Journal of Marine Engineering and Technology, No. 12/2008, pp. 3–7.10.1080/20464177.2008.11020209Search in Google Scholar

13. Haining Liu, Chengliang Liu, Yixiang Huang: Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mechanical Systems and Signal Processing 2011, 25 pp. 558–574.10.1016/j.ymssp.2010.07.019Search in Google Scholar

14. Hongkai Jiang, Qiushi Cai, Huiwei Zhao, Zhiyong Meng: Rolling bearing fault feature extraction under variable conditions using hybrid order tracking and EEMD. Journal of Vibroengineering 2016, Vol. 18, Issue 7, pp. 4449–4457.10.21595/jve.2016.17189Search in Google Scholar

15. Izydorczyk J., Pionka G., Tyma G.: Theory of Signals. Introduction. II corrected and completed edition. HELION, Gliwice 2006.Search in Google Scholar

16. Kostek R., Landowski B., Muślewski Ł.: Simulation of rolling Bering vibration in diagnostics. Journal of Vibroengineering, 2015, Issue 8, Volume 17.Search in Google Scholar

17. Lal M., Riwari R.: Multi-fault identification in simple rotor-bearing-coupling systems based on forced response measurements. Mechanism and Machine Theory 2012, Vol. 51, pp. 87–109.10.1016/j.mechmachtheory.2012.01.001Search in Google Scholar

18. Landowski B, Pająk M, Żółtowski B, Muślewski Ł.: Method of building a model of operational changes for the marine combustion engine describing the impact of the damages of this engine on the characteristics of its operation process. Polish Maritime Research, 2017 no 4 (96), vol. 24, pp. 67–76.10.1515/pomr-2017-0137Search in Google Scholar

19. Leski J. M.: Fuzzy c-ordered-means clustering. Fuzzy Sets and Systems 2016, 286, pp. 114–133.10.1016/j.fss.2014.12.007Search in Google Scholar

20. Li B., Mo-Yuen C., Yodyium T., et al.: Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on industrial electronics, 2000, 47(5): pp. 1060–1069.10.1109/41.873214Search in Google Scholar

21. Lin J., Zhao M.: Dynamic signal analysis for speed-varying machinery: A review. Scientia Sinica 2015, Vol. 45, Issue 7, pp. 669.10.1360/N092014-00425Search in Google Scholar

22. Liu Z.W., Wei G., Hu J.H., Ma W.S.: A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT. KPCA and Twin SCM 2017, Vol. 66, pp. 249–261.10.1016/j.isatra.2016.11.00127837907Search in Google Scholar

23. Liu Y., Yang G., Li M., et al.: Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Processing 2016, Vol. 125, Issue C, pp. 349–364.10.1016/j.sigpro.2016.02.011Search in Google Scholar

24. Miin-Shen Y., Nataliania Y.: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognition 2017, 71, pp. 45–59.10.1016/j.patcog.2017.05.017Search in Google Scholar

25. Molland A.F.: The Maritime Engineering Reference Book, A Guide to Ship Design, Construction and Operation, Butterworth-Heinemann Oct. 13, 2011.Search in Google Scholar

26. Muślewski Ł.: Evaluation Method of Transport Systems Operation Quality. Polish Journal of Environmental Studies, Vol. 18, No. 2A, Hard Olsztyn 2009.Search in Google Scholar

27. Muślewski Ł., Pająk M., Grządziela A., Musiał J.: Analysis of vibration time histories in the time domain for propulsion systems of minesweepers. Journal of Vibroengineering, 2015 Issue 3, Volume 17, pp. 1309–1316.Search in Google Scholar

28. Muślewski Ł., Pająk M., Landowski B., Żółtowski B.: A method for determining the usability potential of ship steam boilers. Polish Maritime Research 4(92) 2016 Vol. 23, pp. 105–112.10.1515/pomr-2016-0076Search in Google Scholar

29. Pająk M.: The space of a feature of a complex technical system. Scientific Problems of Machines Operation and Maintenance, 2/2010, ITeE Radom 2010, pp. 31–41.Search in Google Scholar

30. Pająk M.: Types of the states space of complex technical systems. Journal of KONES vol. 18 No. 3, Warsaw 2011, pp. 323–331.Search in Google Scholar

31. Pająk M.: The technical states’ space in the modelling process of operation tasks of a complex technical system. Maintenance Problems, 1/2014, ITeE Radom 2014, pp. 15–33.Search in Google Scholar

32. Pająk M.: Operation and service processes expressed in the technical states space of a system. Maintenance Problems, 1/2016, ITeE Radom 2016, pp. 65–81.Search in Google Scholar

33. Pająk M.: Identification of the operating parameters of a complex technical system important from the operational potential point of view. Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 2018 Issue 1, Volume 232 pp. 62–78.10.1177/0959651817735771Search in Google Scholar

34. Peng Wang, Taiyong Wang: Energy weighting method and its application to fault diagnosis of rolling bearing. Journal of Vibroengineering 2017, Vol. 19, Issue 1, pp. 223–236.10.21595/jve.2016.17338Search in Google Scholar

35. Piegat A.: Fuzzy modelling and control. EXIT, Warszawa 1999.Search in Google Scholar

36. Rafiee J., Tse P.W., Harifi A., et al.: A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system. Expert Systems with Applications 2009, 36, pp. 4862–4875.10.1016/j.eswa.2008.05.052Search in Google Scholar

37. Qiu H., Lee J., Lin J., et al.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, 2006 (289), pp. 1066–1090.10.1016/j.jsv.2005.03.007Search in Google Scholar

38. Ruqiang Yan R., Gao R.X., Chen X.: Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 2014 (96), pp. 1–15.10.1016/j.sigpro.2013.04.015Search in Google Scholar

39. Saidi L., Ali J. B., Bechhoefer E., Benbouzid M.: Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics 2017, Vol. 120, pp. 1–8.10.1016/j.apacoust.2017.01.005Search in Google Scholar

40. Shih M., Doctor F., Fan S., Jen K., Shieh J.: Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert-Huang Transform Applied to Depth of Anaesthesia. Entropy 2015, Vol. 17, Issue 3, pp. 928–949.10.3390/e17030928Search in Google Scholar

41. Stefański T.: Steering Theory. Discrete Systems, Nonlinear Stochastic Processes, Static and Dynamic Optimization. Kielce University of Technology, Tom II, Kielce 2001.Search in Google Scholar

42. Szabatin J.: Basics of Theory of Signals. WKŁ, Warszawa 2007.Search in Google Scholar

43. Taria L., Barala Ch., Kimab S.: Fuzzy c-means clustering with prior biological knowledge. Journal of Biomedical Informatics 2009, Volume 42, Issue 1, pp. 74–81.10.1016/j.jbi.2008.05.009267350318595779Search in Google Scholar

44. Wang H.C., Chen J., Dong G.M.: Feature extraction of rolling bearing’s early weak fault based on EEMD and tuneable Q-factor wavelet transform. Mechanical Systems and Signal Processing 2014, Vol. 48, pp. 103–119.10.1016/j.ymssp.2014.04.006Search in Google Scholar

45. Wang T.Y., Chu F.L., Han Q.K., Kong Y.: Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods. Journal of Sound and Vibration 2017, Vol. 392, pp. 367–381.10.1016/j.jsv.2016.12.041Search in Google Scholar

46. Wang Y.X., Xiang J.W., Markert R., Liang M.: Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing 2016, Vol. 66–67, pp. 679–698.10.1016/j.ymssp.2015.04.039Search in Google Scholar

47. Woropay M., Landowski B., Neubauer A.: Controlling reliability in the transport systems. B.P.E.-WIEM, Bydgoszcz– Radom 2004.Search in Google Scholar

48. Woropay M., Muślewski Ł.: Quality in a systemic approach. ITeE, Radom 2005.Search in Google Scholar

49. Wu G. W., Wang C. M., Bao J. D., et al.: A wavelet threshold de-noising algorithm based on adaptive threshold function. Journal of Electronics & Information Technology 2014, Vol. 36, Issue 6, pp. 1340–1347.Search in Google Scholar

50. Xiaoming Xue, Jianzhong Zhou, Yanhe Xu, Wenlong Zhu, Chaoshun Li: An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing 2015, 62–63, pp. 444–459.10.1016/j.ymssp.2015.03.002Search in Google Scholar

51. Xiaoyun Gong, Wenliao Du, Anthimos Georgiadis, Baowei Zhao: Identification of multi-faults in rotor-bearing system using spectral kurtosis and EEMD. Journal of Vibroengineering 2017, Vol. 19 Issue 7, pp. 5036–5046.10.21595/jve.2017.18671Search in Google Scholar

52. Xi-hui Chen, Gang Cheng, Xian-lei Shan, Xiao Hu, Qiang Guo, Hou-guang Liu: Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance. Measurement 2015, 73, pp. 55–67.10.1016/j.measurement.2015.05.007Search in Google Scholar

53. Yu Fajun, Fan Fuling, Wang Shuanghong, Zhou Fengxing: Transform-domain sparse representation based classification for machinery vibration signals. Journal of Vibroengineering 2018, Vol. 20, Issue 2, pp. 979–987.10.21595/jve.2017.18865Search in Google Scholar

54. Zerroukia A.A., Aifac T., Baddaria, K.: Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field Algeria. Journal of Petroleum Science and Engineering 2014, 115, pp. 78–89.10.1016/j.petrol.2014.01.011Search in Google Scholar

55. Zhang L. L., Zeng R.L., Jia J.D., et al.: Engine fault diagnosis based on work-cycle order tracking spectrum and fuzzy c-mean clustering. Automotive Engineering 2014, Vol. 36, Issue 8, pp. 1024–1028.Search in Google Scholar

56. Zhang D.C., Yu D.J.: Multi-fault diagnosis of gearbox based on resonance-based signal sparse decomposition and comb filter. Measurement 2017, Vol. 103, pp. 361–369.10.1016/j.measurement.2017.03.006Search in Google Scholar

57. Zhao M., Lin J., Wang X., et al.: A tacho-less order tracking technique for large speed variations. Mechanical Systems & Signal Processing 2013, Issue 1, pp. 76–90.10.1016/j.ymssp.2013.03.024Search in Google Scholar

58. Zhiquan Qi, Yingjie Tian, Yong Shi: Robust twin support vector machine for pattern classification. Pattern Recognition 2013, 46, pp. 305–316.10.1016/j.patcog.2012.06.019Search in Google Scholar

59. Zieliński T.P.: Digital Processing of Signals. From theory to practice. WKŁ, Warszawa 2009.Search in Google Scholar