Volume 20, Issue 8 (August 2020)                   Modares Mechanical Engineering 2020, 20(8): 1991-2000 | Back to browse issues page

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Meserkhani A, Jafari S, Rahi A. Experimental Detection and Size Classification of Defects on Outer Race of Angular Contact Ball Bearing Using Acoustic Emission Signals with Artificial Neural Network. Modares Mechanical Engineering 2020; 20 (8) :1991-2000
URL: http://mme.modares.ac.ir/article-15-35822-en.html
1- Applied Design Department, Mechanical & Energy Systems Engineering Faculty, Shahid Beheshti University, Tehran, Iran
2- Applied Design Department, Mechanical & Energy Systems Engineering Faculty, Shahid Beheshti University, Tehran, Iran , m_jafari@sbu.ac.ir
Abstract:   (1945 Views)
In this paper, experimental defect diagnosis and the classification of its size in the outer race of angular contact ball bearing with acoustic emission method and artificial neural network are presented. In an experimental system, bearings are loaded at four speeds of 600, 900, 1200, and 1500rpm with four loads from low to high. Loads are applied to the outer race with the help of four bolts with equal and specific torques. Since the bearing is angular type, this type of loading is converted to radial and axial combined loading simultaneously and differs from conventional loads in deep groove bearings. Acoustic emission waves are recorded using broadband sensors in two states, healthy and defective. Therefore, to diagnose the defect, different states can be compared with the healthy. The spark method was used to create an artificially seeded defect. In analyzing the results, a new parameter called “the total time above threshold” was introduced to increase the efficiency of defect diagnosis and classification of its size. With the help of the introduced parameter and 4 conventional acoustic emission parameters and using an artificial neural network, the performance of the artificial intelligence system was 95.1% in defect diagnosis and 94.4% in defect size classification.
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Article Type: Original Research | Subject: Mechatronics
Received: 2019/09/11 | Accepted: 2020/04/27 | Published: 2020/08/15

1. Sachse W, Kim KY. Quantitative acoustic emission and failure mechanics of composite materials. Ultrasonics. 1987;25(4):195-203 [Link] [DOI:10.1016/0041-624X(87)90033-3]
2. Inaba H, Nakamura H, Enoki M, Nakano M, Shigeishi M, Yuyama S, et al. Practical acoustic emission testing. Tokyo: Springer; 2016. [Link]
3. Mba D. The use of acoustic emission for estimation of bearing defect size. Journal of Failure Analysis and Prevention. 2008;8(2):188-192. [Link] [DOI:10.1007/s11668-008-9119-8]
4. Mba D. Acoustic emissions and monitoring bearing health. Tribology Transactions. 2003;46(3):447-451. [Link] [DOI:10.1080/10402000308982649]
5. Taha Z, Widiyati K. Artificial neural network for bearing defect detection based on acoustic emission. The International Journal of Advanced Manufacturing Technology. 2010;50(1-4):289-296. [Link] [DOI:10.1007/s00170-009-2476-y]
6. Li CJ, Li SY. Acoustic emission analysis for bearing condition monitoring. Wear. 1995;185(1-2):67-74. [Link] [DOI:10.1016/0043-1648(95)06591-1]
7. Elasha F, Greaves M, Mba D, Addali A. Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox. Procedia CIRP. 2015;38:30-36. [Link] [DOI:10.1016/j.procir.2015.08.042]
8. Kim YH, Tan ACC, Yang BS. Parameter comparison of acoustic emission signals for condition monitoring of low-speed bearings. Australian Journal of Mechanical Engineering. 2008;6(1):45-52. [Link] [DOI:10.1080/14484846.2008.11464556]
9. Al-Ghamd AM, Mba D. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mechanical Systems and Signal Processing. 2006;20(7):1537-1571. [Link] [DOI:10.1016/j.ymssp.2004.10.013]
10. Choudhury A, Tandon N. Application of acoustic emission technique for the detection of defects in rolling element bearings. Tribology International. 2000;33(1):39-45. [Link] [DOI:10.1016/S0301-679X(00)00012-8]
11. Samanta B, Al-Balushi KR, Al-Araimi SA. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence. 2003;16(7-8):657-665. [Link] [DOI:10.1016/j.engappai.2003.09.006]
12. Van Hecke B, Yoon J, He D. Low speed bearing fault diagnosis using acoustic emission sensors. Applied Acoustics. 2016;105:35-44. [Link] [DOI:10.1016/j.apacoust.2015.10.028]
13. Mirhadizadeh SA, Moncholi EP, Mba D. Influence of operational variables in a hydrodynamic bearing on the generation of acoustic emission. Tribology International 2010;43(9):1760-1767. [Link] [DOI:10.1016/j.triboint.2010.03.003]
14. Couturier J, Mba D. Operational bearing parameters and acoustic emission generation. Journal of Vibration and Acoustics. 2008;130(2):024502. [Link] [DOI:10.1115/1.2776339]
15. Al-Ghamdi AM, Cole P, Such R, Mba D. Estimation of bearing defect size with acoustic emission. The British Institute of Non-Destructive Testing. 2004;46(12):758-761. [Link] [DOI:10.1784/insi.46.12.758.54491]
16. Elforjani M, Shanbr S. Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Transactions on Industrial Electronics. 2017;65(7):5864-5871. [Link] [DOI:10.1109/TIE.2017.2767551]
17. Hosseini S, Ahmadinajafabadi M, Akhlaghi M. Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm. Tribology International. 2016;95:426-434. [Link] [DOI:10.1016/j.triboint.2015.11.045]
18. Chacon JLF, Kappatos V, Balachandran W, Gan TH. A novel approach for incipient defect detection in rolling bearings using acoustic emission technique. Applied Acoustics. 2015;89:88-100. [Link] [DOI:10.1016/j.apacoust.2014.09.002]
19. Sharma RB, Parey A. Modelling of acoustic emission generated in rolling element bearing. Applied Acoustics. 2019;144:96-112. [Link] [DOI:10.1016/j.apacoust.2017.07.015]
20. Kumar S, Goyal D, Dhami SS. Statistical and frequency analysis of acoustic signals for condition monitoring of ball bearing. Materials Today: Proceedings. 2018;5(2 Pt 1):5186-5194. [Link] [DOI:10.1016/j.matpr.2017.12.100]
21. Prosvirin A, Kim J, Kim JM. Bearing fault diagnosis based on convolutional neural networks with kurtogram representation of acoustic emission signals. In: Park DS, Chao HCh, Jeong YS, Park JJ. Advances in Computer Science and Ubiquitous Computing. Singapore: Springer; 2017. [Link] [DOI:10.1007/978-981-10-7605-3_4]

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