Title: Application of optimised neural networks models in gears and bearings faults diagnosis

Authors: Kaaïs Khoualdia; Elias Hadjadj Aoul; Tarek Khoualdia

Addresses: Faculty of Science and Technology, Department of Mechanical Engineering, Souk Ahras University, Souk Ahras 41 000, Algeria ' Department of Electromechanical, Electromechanical System Laboratory, Annaba University, Annaba 23 000, Algeria ' Faculty of Science and Technology, Department of Mechanical Engineering, Souk Ahras University, Souk Ahras 41 000, Algeria

Abstract: Gears and bearings are some of the most important machine components in the industrial world and detection of their faults has become a major trend. In the present article, in order to bring a reliable methodology for monitoring and diagnosis of rotating machinery failures, a test rig is implemented. However, to diagnose gears and bearings combined faults, a monitoring system based on neural network model (NNM), is proposed. To train and test the NNM, the principal high frequency indicators, determined with the collected time domain vibration data, and codes of defects are used respectively as input and output data. A comparison of two learning algorithms, optimised by the Taguchi method, was done to determine the best NNM. Therefore, the proposed method is effective to study other various industrial cases.

Keywords: gear and bearing combined defects; fault vibration analysis; ANN; artificial neural network; design of experiment; Taguchi method.

DOI: 10.1504/IJVNV.2020.112430

International Journal of Vehicle Noise and Vibration, 2020 Vol.16 No.1/2, pp.30 - 45

Received: 29 Jul 2019
Accepted: 25 Oct 2019
Published online: 15 Jan 2021 *

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