Authors:
Abdoulhdi A. Borhana,Uma Shankar,R. Kalaivani,M.A. Khattak,Yasir Hassan Ali,Omar Suliman Zaroog,DOI NO:
https://doi.org/10.26782/jmcms.spl.9/2020.05.00022Keywords:
Ball bearing,early fault detection,time domain technique,inner raceways,Abstract
One of the most important assets in an industry would be rotating machines. The reliability and availability are very crucial in order to support the accomplishment of an industry field. Major and even minor faults in rotating machines cause a decrease in both productivity and cost efficiency. Various methods have been studied by researcher and introduced in the industry for the detection of an early fault in rotating machines. Vibration signal analysis is one of a standout amongst other methods. This research paper focused on early fault detection in the bearing component at two different positions; inner raceway and ball. The faults were established at three different diameters of 0.007 inches, 0.021 inches, and 0.028 inches. By utilizing time domain technique, parameters such as mean, median, standard deviation, RMS, skewness, impulse factor and shape factor were determined. The vibration signal for both healthy and faulty bearing was deliberated by using the MATLAB software. All the data obtained were represented in graphs where the healthy and faulty bearing values were compared and analyzed.Refference:
I. Csegroups.case.edu. (2017). Download a Data File | Bearing Data Center. [online] Available at: http://csegroups.case.edu/bearingdatacenter/pages/download-data-file [Accessed 31 Aug. 2017].
II. Igba, J., Alemzadeh, K., Durugbo, C. and Eiriksson, E. (2016). Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes. Renewable Energy, 91, 90-106. doi: 10.1016/j.renene.2016.01.006
III. Jiang, Q., Shen, Y., Li, H. and Xu, F. (2018). New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network. Sensors, 18(2), 337. doi: 10.3390/s18020337
IV. Liu, W.Y., Tang, B.P., Han, J.G., Lu, X.N., Hu, N.N. and He, Z.Z. (2015). The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renew. Sustain. Energy Rev. 44, 466–472.
V. Muszynska, A. (1995). Vibrational Diagnostics of Rotating Machinery Malfunctions. International Journal Of Rotating Machinery, 1(3-4), 237-266. doi: 10.1155/s1023621x95000108
VI. Shukla, S. and Karma, V. (2014). Fault Detection of Two Stage Spur Gearbox using Time Domain Technique: Effect of Tooth Breakage and Improper Chamfering. International Journal of Innovative Science, Engineering & Technology, Vol. 1(Issue 4). ISSN 2348 – 7968
VII. Soleimani, A. and Khadem, S. (2015). Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets. Chaos, Solitons& Fractals, 78, 61-75. doi: 10.1016/j.chaos.2015.06.018
VIII. TabriziZarringhabaei, A.A. (2015). Development of new fault detection methods for rotating machines (roller bearings) (PhD Thesis). Mechanical and Aerospace Engineering Department, Porto Institutional Repository, Politenico di Torino.
IX. Tatis De leon, R. (2012). Vibration Measurement for Rotatory Machines (Degree Programme in Automation Engineering). HAMK University of Applied Science.
X. Zayeri, R., Attaran, B., Ghanbarzadeh, A. and Moradi, S. (2011). Artificial Neural Network Based Fault Diagnostics of Rolling Element bearings using Continuous Wavelet Transform. The 2Nd International Conference on Control, Instrumentation, and Automation (IEEE), At Shiraz University, Iran. doi: 10.1109/ICCIAutom.2011.6356754
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