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Machine hearing for industrial fault diagnosis

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conference contribution
posted on 03.08.2021, 13:24 by Eve ZhangEve Zhang, Miguel Martinez-GarciaMiguel Martinez-Garcia
This paper proposes to apply a machine hearing framework for industrial fault diagnosis, which is inspired by humans' 'listening and diagnostic' capability in identifying machinery faults. The proposed method combines simplified human auditory functionalities with machine learning, aiming to model in a more biologically plausible way. It includes primarily using cochleagram to extract useful time-frequency information in sound signals-representing the cochlea filtering properties in human hearing. Then, a recurrent neural network with long short-term memory layers is constructed to learn and classify the cochleagrams for fault diagnosis-this is to incorporate memory elements in temporal information processing. The proposed method is validated with an experimental study on bearing fault diagnosis using acoustic measurements, while the developed machine hearing scheme could be beneficial to many industrial fault diagnosis applications, e.g., for aeronautical, automotive, marine, railway and manufacturing industry.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

Pages

849 - 854

Source

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2020-10-08

Copyright date

2020

ISBN

9781728169040

eISSN

2161-8089

Language

en

Location

Hong Kong, China (Online)

Event dates

20th August 2020 - 21st August 2020

Depositor

Dr Eve Zhang. Deposit date: 29 July 2021