Fuzzy identification of the reliability state of the mine detecting ship propulsion system

• Autorzy: Pajak M. , Muslewski L. , Landowski B. , Grzadziela A.
• Języki publikacji: EN

Abstrakty

EN

The study presents the evaluation and comparative analysis of engine shaft line performance in maritime transport ships of the same type. During its operation, a technical system performs functions for which it was designed. It goes through different states. Dynamic state changes of a rotational system can be identified by means of its vibration measurement. For this purpose, a research was carried out which involved recording vibrations of the analysed rotational systems. The recordings were used for calculating selected characteristics in the time-domain, where one of the most unique is the value of the normalized mutual correlation function. On the basis of the concentration values, the characteristics which unambiguously determine the ability state were selected for further studies. Then an identification method for rotational system non-coaxiality was proposed. The method involves using fuzzy clustering. According to this method the values of input signal characteristics were used to formulate fuzzy clusters of system ability and inability states. The method can be used for identifying the current state of the system. The study presents the results of the application of this method in engine turbine shaft lines of minesweepers, with the rotational system selected as an example. It needs to be noted that the efficiency of identifying the operating state of the system with this method is higher than with other methods described in the literature by authors who deal with this issue. The research results have a significant impact on the evaluation of mechanical properties of the studied objects and directly affect operational states of mechanical systems, including those installed in minesweepers, thus determining their reliability

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2019
• Tom: 26
• Numer: 1
• Opis fizyczny: p.55-64,fig.,ref.

Twórcy

autor

Pajak M.

• University of Technology and Humanities, Stasieckiego 54, 26-600 Radom, Poland

autor

Muslewski L.

• University of Technology and Life Science, Kaliskiego 7, 85-796 Bydgoszcz, Poland

autor

Landowski B.

• University of Technology and Life Science, Kaliskiego 7, 85-796 Bydgoszcz, Poland

autor

Grzadziela A.

• , Polish Naval Academy, Smidowicza 69, 81-103 Gdynia, Poland

Bibliografia

• 1. Antoni J.: Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration 2007 (304), pp. 497–529.
• 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.
• 3. Bielawski, P.: Diagnostics of marine propeller shafts. Journal of Polish CIMAC, 2011 Vol. 6 No. 2, pp. 31-40.
• 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.
• 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.
• 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.
• 7. Gang Tang, QinYang, Hua-Qing Wang, Gang-gang Luo, Jian-wei Ma: Sparse classification of rotating machinery faults based on compressive sensing strategy. Mechatronics 2015, Volume 31, pp. 60–67.
• 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.
• 9. Górniewicz I., Roman S.: Mathematical Analysis for Physicians. Scientific. Publishing Office of Nicolaus Copernicus University of Torun, Torun 2000.
• 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.
• 11. Grządziela A. Muślewski Ł.: High quality simulation of the effects of underwater detonation impact. Journal of Vibroengineering, 2013, Issue 1, Volume 15.
• 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.
• 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.
• 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.
• 15. Izydorczyk J., Pionka G., Tyma G.: Theory of Signals. Introduction. II corrected and completed edition. HELION, Gliwice 2006.
• 16. Kostek R., Landowski B., Muślewski Ł.: Simulation of rolling Bering vibration in diagnostics. Journal of Vibroengineering, 2015, Issue 8, Volume 17.
• 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.
• 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.
• 19. Leski J. M.: Fuzzy c-ordered-means clustering. Fuzzy Sets and Systems 2016, 286, pp. 114–133.
• 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.
• 21. Lin J., Zhao M.: Dynamic signal analysis for speed-varying machinery: A review. Scientia Sinica 2015, Vol. 45, Issue 7, pp. 669.
• 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.
• 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.
• 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.
• 25. Molland A.F.: The Maritime Engineering Reference Book, A Guide to Ship Design, Construction and Operation, Butterworth-Heinemann Oct. 13, 2011.
• 26. Muślewski Ł.: Evaluation Method of Transport Systems Operation Quality. Polish Journal of Environmental Studies, Vol. 18, No. 2A, Hard Olsztyn 2009.
• 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.
• 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.
• 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.
• 30. Pająk M.: Types of the states space of complex technical systems. Journal of KONES vol. 18 No. 3, Warsaw 2011, pp. 323–331.
• 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.
• 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.
• 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.
• 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.
• 35. Piegat A.: Fuzzy modelling and control. EXIT, Warszawa 1999.
• 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.
• 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.
• 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.
• 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.
• 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.
• 41. Stefański T.: Steering Theory. Discrete Systems, Nonlinear Stochastic Processes, Static and Dynamic Optimization. Kielce University of Technology, Tom II, Kielce 2001.
• 42. Szabatin J.: Basics of Theory of Signals. WKŁ, Warszawa 2007.
• 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.
• 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.
• 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.
• 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.
• 47. Woropay M., Landowski B., Neubauer A.: Controlling reliability in the transport systems. B.P.E.-WIEM, Bydgoszcz–Radom 2004.
• 48. Woropay M., Muślewski Ł.: Quality in a systemic approach. ITeE, Radom 2005.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 58. Zhiquan Qi, Yingjie Tian, Yong Shi: Robust twin support vector machine for pattern classification. Pattern Recognition 2013, 46, pp. 305–316.
• 59. Zieliński T.P.: Digital Processing of Signals. From theory to practice. WKŁ, Warszawa 2009.

Identyfikatory

DOI

10.2478/pomr-2019-0007