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:   (2259 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

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