Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines

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

Abstract

A new acoustic feature representation is proposed to detect faults of rotary machinery using acoustic signals. Acoustic features include the amplitude, frequency, and timbre, with the former two often used as diagnostics features. The timbre is also an indicator of the abnormal operation of machinery. The present study therefore focuses on the use of timbre-based features. Changes in stationary parts of observed acoustic signals (e.g., a change in period, which corresponds to the number of rotations) can be considered a physical quantity of the timbre. Because a rotary machine normally operates at a certain rotational speed, differences in the rotational period between adjacent frames are ideally zero in such normal operation. In contrast, the period differences should not be zero for a machine with a fault because observed signals contain characteristics of both the normal and faulty states and these characteristics vary over time. The present study therefore attempts to exploit the period difference of acoustic signals as a feature representation for fault detection. Experimental analysis conducted using acoustic signals recorded by microphones demonstrates that the proposed feature extraction contributes to the fault detection of rotary machines.

Original languageEnglish
Title of host publicationProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
EditorsDian Wang, Yong Zhou, Diego Cabrera, Chuan Li, Chunlin Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages302-305
Number of pages4
ISBN (Electronic)9781538660577
DOIs
Publication statusPublished - 2019 Mar 11
Event2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 - Xi'an, China
Duration: 2018 Aug 152018 Aug 17

Publication series

NameProceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018

Conference

Conference2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
CountryChina
CityXi'an
Period18/8/1518/8/17

Fingerprint

fault detection
Fault Detection
Fault detection
Acoustics
acoustics
machinery
Fault
Rotating machinery
Zero
Experimental Analysis
Microphones
microphones
pattern recognition
Feature Extraction
Machinery
Feature extraction
Diagnostics
Adjacent
Vary
Demonstrate

Keywords

  • Acoustic diagnosis
  • Fault detection
  • Feature extraction
  • Rotary machine
  • Timbre

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

Cite this

Minemura, K., Ogawa, T., & Kobayashi, T. (2019). Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines. In D. Wang, Y. Zhou, D. Cabrera, C. Li, & C. Zhang (Eds.), Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 (pp. 302-305). [8664991] (Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SDPC.2018.8664991

Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines. / Minemura, Kesaaki; Ogawa, Tetsuji; Kobayashi, Tetsunori.

Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018. ed. / Dian Wang; Yong Zhou; Diego Cabrera; Chuan Li; Chunlin Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 302-305 8664991 (Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018).

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

Minemura, K, Ogawa, T & Kobayashi, T 2019, Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines. in D Wang, Y Zhou, D Cabrera, C Li & C Zhang (eds), Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018., 8664991, Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 302-305, 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018, Xi'an, China, 18/8/15. https://doi.org/10.1109/SDPC.2018.8664991
Minemura K, Ogawa T, Kobayashi T. Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines. In Wang D, Zhou Y, Cabrera D, Li C, Zhang C, editors, Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 302-305. 8664991. (Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018). https://doi.org/10.1109/SDPC.2018.8664991
Minemura, Kesaaki ; Ogawa, Tetsuji ; Kobayashi, Tetsunori. / Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines. Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018. editor / Dian Wang ; Yong Zhou ; Diego Cabrera ; Chuan Li ; Chunlin Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 302-305 (Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018).
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