Journal of Vibration Testing and System Dynamics
Fast Unbalancing of Rotating Machines by Combination of Computer Vision and Vibration Data Analysis
Journal of Vibration Testing and System Dynamics 1(4) (2017) 343--352 | DOI:10.5890/JVTSD.2017.12.005
A. Najedpak; C. Yang
Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202-8359, USA
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Abstract
Unbalance is one of the most common mechanical faults in rotating machines. Although different balancing methods have been developed, most of them require balancing machine to perform unbalance correction. A method using accelerometers data and intricate vibration theories can eliminate the need of balancing machine, and the amplitude and phase of the machine’s vibrations can be identified. However it needs numerous measurements, and in some cases it is even impossible to be implemented. To overcome this problem, a novel approach with reduced number of measurements is presented in this paper. The proposed method requires only two measurements: one from original unbalanced condition, and the other from modified situation after adding an arbitrary trial mass to a marked location. The rotating rotor is being video recorded under original unbalanced and modified situations. The position of the marked area is identified when the amplitude of the sinusoidal vibration response reaches the maximum. The correction mass and its adding location are calculated using proposed method. To demonstrate the effectiveness of our method, an experiment is setup. Vibrations under healthy, unbalanced and balanced conditions are analyzed. The results demonstrated that the developed method is more cost effective with the same accuracy as the other contested balancing techniques.
Acknowledgments
The work reported in this paper was funded by UND ME department and UND VPAA New Faculty Start-up Award 43700-2725-UND0031020.
References
-
[1]  | Nejadpak, A. and Yang, C. (2016), A vibration-based diagnostic tool for analysis of superimposed failures in electric machines, IEEE International Conference on Electro Information Technology (EIT). |
-
[2]  | Renwick, J.T. and Babson, P.E. (1985), Vibration Analysis—A Proven Technique as a PredictiveMaintenance Tool, IEEE Transactions on Industry Applications, 2(1985), 324-332. |
-
[3]  | Tsypkin, M. (2011), Induction motor condition monitoring: Vibration analysis technique-A practical implementation, IEEE International Electric Machines & Drives Conference. |
-
[4]  | Copping, M. (2015), Vibration analysis reporting–bearing failure stages and responses, Reliabilityweb.com. |
-
[5]  | Plante, T., Nejadpak, A., and Yang, C. (2015), Faults detection and failures prediction using vibration analysis, IEEE AUTOTESTCON. |
-
[6]  | Mehala, N. (2010), Condition monitoring and fault diagnosis of induction motor using motor current signature analysis, Diss. National Institute of Technology Kurukshetra, India. |
-
[7]  | Ebersbach, S. and Peng, Z. (2018), Expert system development for vibration analysis in machine condition monitoring, Expert Systems with Applications, 34(1), 291-299. |
-
[8]  | Plante, T., Stanley, L, Nejadpak, A., and and Yang, C. (2016), Rotating machine fault detection using principal component analysis of vibration signal, IEEE AUTOTESTCON. |
-
[9]  | Nisbett, K. (1996), Dynamic balancing of Rotating Machinery Experiment, Technical Manual. http://web.mst.edu/~stutts/ME242/LABMANUAL/DynamicBalancingExp.pdf. Accessed on April 5, 2017. |
-
[10]  | Canny, J. (1986), A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 679-698. |
-
[11]  | Gao, W., Zhang, X., Zhang, L., and Liu, H. (2010), An improved Sobel edge detection, 2010 3rd IEEE International Conference on Computer Science and Information Technology, 5, IEEE, 2010. |
-
[12]  | Shrivakshan, G.T. and Chandrasekar, C. (2012), A comparison of various edge detection techniques used in image processing, International Journal of Computer Science Issues, 9(5), 272-276. |
-
[13]  | Duda, R.O. and Hart, P.E. (1972), Use of the Hough transformation to detect lines and curves in pictures, ommunications of the ACM, 15(1), 11-15. |