Iranian Journal of Mechanical Engineering Transactions of ISME

Iranian Journal of Mechanical Engineering Transactions of ISME

Design of the L90 automobile gearbox fault detection system using the audio signal analysis

Authors
Mohammad Zadshakoyan
Abolfazl Gholami
Department of Mechanical Production Production, Faculty of Mechanics, Tabriz University, Tabriz  
10.30506/ijmep.2021.136715.1730
Abstract
Gearboxes may be defective during the manufacturing, assembly or operation process. In gearbox manufacturing factories, it is important to identify defective gearboxes to prevent them from entering the consumption cycle. Audio signals indicate the operation of the gearbox and the condition of its internal components. Therefore, this system is a powerful method to diagnose the gearbox healthy or defective in different gears. In this study, intelligent gearbox troubleshooting using audio signals has been done. After receiving the audio signals, suitable algorithms for troubleshooting were proposed and based on that, the troubleshooting process was performed. In this test, the gearbox of the Thunder 90 passenger car in the assembly line was checked for the health or defect. The received audio signals are first processed in the time, frequency (fast Fourier transform) and time-frequency (discrete wavelet transform) domains. Then the feature was extracted from the processed signals in all three domains of time, frequency and time-frequency; Then, using the error test method and statistical inference method, the desired characteristics were selected for use in classification. Then, using various methods of artificial neural network classification and support vector machine, the intelligent diagnostic process was performed. The results showed that the backup machine classification method on data processed with discrete wavelet transform has an average error of less than 9%. This error is less than the error of other signal processing and classification methods in this experiment.
Keywords
Audio Detection
Wavelet Packet Transform
Suport Vector Machine
Artificial Neural Network

Subjects

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  • Receive Date 20 September 2020
  • Revise Date 22 April 2021
  • Accept Date 27 April 2021