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 language | English |
|---|---|
| Title of host publication | Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
| Editors | Dian Wang, Yong Zhou, Diego Cabrera, Chuan Li, Chunlin Zhang |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 302-305 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538660577 |
| DOIs | |
| Publication status | Published - 2019 Mar 11 |
| Event | 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 - Xi'an, China Duration: 2018 Aug 15 → 2018 Aug 17 |
Publication series
| Name | Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
|---|
Conference
| Conference | 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 |
|---|---|
| Country | China |
| City | Xi'an |
| Period | 18/8/15 → 18/8/17 |
Fingerprint
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
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 proceeding › Conference contribution
}
TY - GEN
T1 - Acoustic Feature Representation Based on Timbre for Fault Detection of Rotary Machines
AU - Minemura, Kesaaki
AU - Ogawa, Tetsuji
AU - Kobayashi, Tetsunori
PY - 2019/3/11
Y1 - 2019/3/11
N2 - 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.
AB - 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.
KW - Acoustic diagnosis
KW - Fault detection
KW - Feature extraction
KW - Rotary machine
KW - Timbre
UR - http://www.scopus.com/inward/record.url?scp=85064140496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064140496&partnerID=8YFLogxK
U2 - 10.1109/SDPC.2018.8664991
DO - 10.1109/SDPC.2018.8664991
M3 - Conference contribution
AN - SCOPUS:85064140496
T3 - Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
SP - 302
EP - 305
BT - Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
A2 - Wang, Dian
A2 - Zhou, Yong
A2 - Cabrera, Diego
A2 - Li, Chuan
A2 - Zhang, Chunlin
PB - Institute of Electrical and Electronics Engineers Inc.
ER -