Crack fault diagnostics plays a critical role for rotating machinery in the traditional and Industry 4.0 factory. In this paper, an experiment is set up to study the dynamic response of a rotor with a breathing crack as it passes through its 1/2, 1/3, 1/4 and 1/5 subcritical speeds. A cracked shaft is made by applying fatigue loads through a three-point bending apparatus and then placed in a rotor testbed. The vibration signals of the testbed during the coasting-up process are collected. Whirl orbit evolution at these subcritical speed zones is analyzed. The Fourier spectra obtained by FFT are used to investigate the internal frequencies corresponding to the typical orbit characteristics. The results show that the appearance of the inner loops and orientation change of whirl orbits in the experiment are agreed well with the theoretical results obtained previously. The presence of higher frequencies 2X, 3X, 4X and 5X in Fourier spectra reveals the causes of subharmonic resonances at these subcritical speed zones. The experimental investigation is more systematic and thorough than previously reported in the literature. The unique dynamic behavior of the orbits and frequency spectra are feasible features for practical crack diagnosis. This paper provides a critical technology support for the self-aware health management of rotating machinery in the Industry 4.0 factory.
1. J Qin, Y Liu, R GROSVENOR. A categorical framework of manufacturing for Industry 4.0 and beyond. Procedia CIRP, 2016, 52:173-178.
2. S Y Wang, J F Wan, D Q ZHANG. Towards smart factory for industry 4.0:a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 2016, 101:158-168.
3. J Lee, B Bagheri, H A Kao. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 2015, 3:18-23.
4. R Harrison, D Vera, B Ahmad. Engineering the smart factory. Chinese Journal of Mechanical Engineering, 2016, 29(6):1046-1051.
5. A Radziwon, A Bilberg, BOGERS M, et al. The smart factory:exploring adaptive and flexible manufacturing solutions. Procedia Engineering, 2014, 69:1184-1190.
6. J Lee. Smart factory systems. Informatik Spektrum, 2015, 38:230-235.
7. S F Qin, V D Vab, E Chatzakis, et al. Exploring barriers and opportunities in adopting crowdsourcing based new product development in manufacturing SMEs. Chinese Journal of Mechanical Engineering, 2016, 29(6):1052-1056.
8. A Hussein, K Cheng. Development of the supply chain oriented quality assurance system for aerospace manufacturing SMEs and its implementation perspectives. Chinese Journal of Mechanical Engineering, 2016, 29(6):1067-1073.
9. S Weyer, M Schmitt, M Ohmer, et al. Towards industry 4.0-standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-Papers Online, 2015, 48-3:579-584.
10. J W Song, D Norman, T Nam, et al. Wireless device connection problems and design solutions. Chinese Journal of Mechanical Engineering, 2016, 29(6):1145-1155.
11. B Bagheri, S Yang, H Kao, J Lee. Cyber-physical systems architecture for self-aware machines in Industry 4.0 environment. IFAC-Papers OnLine, 2015, 48-3:1622-1627.
12. L Cheng, N Li, X Chen, et al. The influence of crack breathing and imbalance orientation angle on the characteristics of the critical speed of a cracked rotor. Journal of Sound and Vibration, 2011, 330:2031-2048.
13. H Z Gao, L Liang, X G Chen, et al. Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization. Chinese Journal of Mechanical Engineering, 2015, 28(1):96-105.
14. S Singh, N Kumar. Combined rotor fault diagnosis in rotating machinery using empirical mode decomposition. Journal of Mechanical Science and Technology, 2014, 28 (12):4869-4876.
15. C Kumar, V Rastogi. A brief review on dynamics of a cracked rotor. International Journal of Rotating Machinery, 2009, 2009(2):1-6.
16. R T Liong, C Proppe. Application of the cohesive zone model for the evaluation of stiffness losses in a rotor with a transverse breathing crack. Journal of Sound and Vibration, 2013, 332:2098-2110.
17. J J Sinou, A W Lees. A non-linear study of a cracked rotor. European Journal of Mechanics A/Solids, 2007, 26:152-170.
18. T R Babu, S Srikanth, A S Sekhar. Hilbert-Huang transform for detection and monitoring of crack in a transient rotor. Mechanical Systems and Signal Processing, 2008, 22:905-914.
19. M A Al-Shudeifat, E A Butcher. New breathing functions for the transverse breathing crack of the cracked rotor system:Approach for critical and subcritical harmonic analysis. Journal of Sound and Vibration, 2011, 330:526-544.
20. M Silani, S Ziaei-rad, H Talebi. Vibration analysis of rotating systems with open and breathing Cracks. Applied Mathematical Modelling, 2013, 37:9907-9921.
21. C Shravankumar, R Tiwari. Detection of a fatigue crack in a rotor system using full-spectrum based estimation. Sādhānaˉ, 2016, 41(2):239-251.
22. Z Y Lu, L Hou, Y S Chen. Nonlinear response analysis for a dualrotor system with a breathing transverse crack in the hollow shaft. Nonlinear Dynamics, 2016, 83:169-185.
23. C Z Guo, M A Al-Shudeifat, J H Yan, et al. Application of empirical mode decomposition to a Jeffcott rotor with a breathing crack. Journal of Sound and Vibration, 2013, 332:3881-3892.
24. M J Gomez, C Castejon, J C Garcia-Prada. Crack detection in rotating shafts based on 3X energy:analytical and experimental analyses. Mechanism and Machine Theory, 2016, 96:94-106.
25. A K Darpe, K Gupta, A Chawla. Transient response and breathing behaviour of a cracked Jeffcott rotor. Journal of Sound and Vibration, 2004, 272:207-243.
26. T Zhou, Z C Sun, J X Xu, et al. Experimental analysis of cracked rotor. Journal of Dynamic Systems, Measurement, and Control, 2005, 127:313-320.
27. Z H Ren, S H Zhou, C H E, et al. Crack fault diagnosis of rotor systems using wavelet transforms. Computers and Electrical Engineering, 2015, 45:33-41.
28. H B Dong, X F Chen, B Li, et al. Rotor crack detection based on high-precision modal parameter identification method and wavelet finite element model. Mechanical Systems and Signal Processing, 2009, 23:869-883.
29. Y L Lin, F L Chu. Numerical and experimental investigations of flexural vibrations of a rotor system with transverse or slant crack. Journal of Sound and Vibration, 2009, 324:107-125.
30. A A Mohammed, R D Neilson, W F Deans, et al. Crack detection in a rotating shaft using artificial neural networks and PSD characterization. Meccanica, 2014, 49:255-266.