Vibration feature extraction using audio spectrum analyzer based machine learning

Jyun Shun Liang, Kerwin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To develop a tool to identify different mechanical vibrations, this paper presents a novel instrument to extract the vibration features of rotating machinery. The instrument consists of an audio spectrum analyzer, a signal processing circuit and a single-board computer. This architecture offers a cost-effective solution for machine status monitoring and analyzing. The experiment results show that the training data collected from audio spectrum analyzer can work well with both KNN and SVM methods to construct accurate machine-learning models with the 95.8% and 97.2% accuracy respectively.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
Subtitle of host publicationInformation and Innovation for Modern Technology, ICICE 2017
EditorsArtde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages381-384
Number of pages4
ISBN (Electronic)9781538632024
DOIs
Publication statusPublished - 2018 Oct 1
Event2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China
Duration: 2017 Nov 172017 Nov 20

Publication series

NameProceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017

Other

Other2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
CountryChina
CityXiamen, Fujian
Period17-11-1717-11-20

Fingerprint

Spectrum analyzers
Learning systems
Feature extraction
Rotating machinery
Printed circuit boards
Signal processing
Networks (circuits)
Monitoring
Costs
Experiments
Vibration
Machine learning
K-nearest neighbor
Machinery
Experiment
Learning model

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

Cite this

Liang, J. S., & Wang, K. (2018). Vibration feature extraction using audio spectrum analyzer based machine learning. In A. D. K-T. Lam, S. D. Prior, & T-H. Meen (Eds.), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017 (pp. 381-384). [8479273] (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICE.2017.8479273
Liang, Jyun Shun ; Wang, Kerwin. / Vibration feature extraction using audio spectrum analyzer based machine learning. Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 381-384 (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017).
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title = "Vibration feature extraction using audio spectrum analyzer based machine learning",
abstract = "To develop a tool to identify different mechanical vibrations, this paper presents a novel instrument to extract the vibration features of rotating machinery. The instrument consists of an audio spectrum analyzer, a signal processing circuit and a single-board computer. This architecture offers a cost-effective solution for machine status monitoring and analyzing. The experiment results show that the training data collected from audio spectrum analyzer can work well with both KNN and SVM methods to construct accurate machine-learning models with the 95.8{\%} and 97.2{\%} accuracy respectively.",
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Liang, JS & Wang, K 2018, Vibration feature extraction using audio spectrum analyzer based machine learning. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017., 8479273, Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017, Institute of Electrical and Electronics Engineers Inc., pp. 381-384, 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017, Xiamen, Fujian, China, 17-11-17. https://doi.org/10.1109/ICICE.2017.8479273

Vibration feature extraction using audio spectrum analyzer based machine learning. / Liang, Jyun Shun; Wang, Kerwin.

Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 381-384 8479273 (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Liang, Jyun Shun

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PY - 2018/10/1

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N2 - To develop a tool to identify different mechanical vibrations, this paper presents a novel instrument to extract the vibration features of rotating machinery. The instrument consists of an audio spectrum analyzer, a signal processing circuit and a single-board computer. This architecture offers a cost-effective solution for machine status monitoring and analyzing. The experiment results show that the training data collected from audio spectrum analyzer can work well with both KNN and SVM methods to construct accurate machine-learning models with the 95.8% and 97.2% accuracy respectively.

AB - To develop a tool to identify different mechanical vibrations, this paper presents a novel instrument to extract the vibration features of rotating machinery. The instrument consists of an audio spectrum analyzer, a signal processing circuit and a single-board computer. This architecture offers a cost-effective solution for machine status monitoring and analyzing. The experiment results show that the training data collected from audio spectrum analyzer can work well with both KNN and SVM methods to construct accurate machine-learning models with the 95.8% and 97.2% accuracy respectively.

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Liang JS, Wang K. Vibration feature extraction using audio spectrum analyzer based machine learning. In Lam ADK-T, Prior SD, Meen T-H, editors, Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 381-384. 8479273. (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017). https://doi.org/10.1109/ICICE.2017.8479273