An automatic pipeline monitoring system using sound information

Structural Control and Health Monitoring

Volumn 17, Issue 1, 2010, Pages 83-97

  Wan, Chunfeng ^a   Mita, Akira ^a   Kume, Takao ^b  

^a KEIO UNIVERSITY   (Japan)

^b TOKYO GAS CO LTD   (Japan)

Author keywords

Classification; MEL frequency cepstral coefficient (MFCC); Monitoring system; Pipeline; Third party damage

Indexed keywords

GROUND CONSTRUCTION; LOW COSTS; MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC); MEL-FREQUENCY CEPSTRAL COEFFICIENTS; MONITORING SYSTEM; PATTERN CLASSIFICATION; PIPELINE DAMAGE; PIPELINE INTEGRITY; PIPELINE MONITORING; POTENTIAL THREATS; PRIVACY PROBLEMS; SOUND RECOGNITION; WATER SUPPLY NETWORKS;

ACCIDENTS; PIPELINES; ROADS AND STREETS; SENSORS; WATER SUPPLY;

MONITORING;

EID: 77249088677     PISSN: 15452255     EISSN: 15452263     Source Type: Journal    
DOI: 10.1002/stc.295     Document Type: Article

References (14)

1. Eiber RJ, Jones DJ, Kramer GS. Outside force causes most natural gas pipeline failures. Oil and Gas Journal 1987; 85:52-57.
2. http://www.saunalahti.fi/ility/Dossiers/Dossier%20Ghislenghein.htm.
3. Hausamann D et al. Monitoring of gas transmission pipelines-a customer driven civil UAV. 5th ODAS Symposium 2003, Toulouse, France, 4-6 June, 2003.
4. Huebler JE. Detection of unauthorized construction equipment in pipeline right-of-ways. Technical Report of Gas Technology Institute, 2004.
5. Qu Z et al. Detection of the abnormal events along the oil and gas pipeline and multi-scale chaotic character analysis of the detected signals. Measurement Science and Technology 2008; 19:1-8.
6. Wu H et al. Vehicle sound signature recognition by frequency vector principal component analysis. IEEE Instrumentation and Measurement Technology Conference, U.S.A., 1998.
7. Cowling M, Sitte R. Comparison of techniques for environmental sound recognition. Pattern Recognition Letters 2003; 24:2895-2907.
8. Peltonen V et al. Computational auditory scene recognition. Proceedings of the IEEE 2002; 2:1941-1944.
9. Ma L, Milner B, Smith D. Acoustic environment classification. ACM Transactions on Speech and Language Processing 2006; 3(2):1-22.
10. Bogert BP, Healy MJR, Tukey JW. The frequency analysis of time series for echoes: cepstrum, pseudoautocovariance, cross-cepstrum, and saphe cracking. In Proceedings of the Symposium on Time Series Analysis, Rosenblatt M (ed.), Chapter 15. Wiley: New York, 1963; 209-243.
11. Goldhor RS. Recognition of Environment Sounds, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-93), Minneapolis, Minnesota, U.S.A., 27-30 April 1993, Vol.1, pp. 142-152.
12. Rabiner L, Juang BH. Fundamentals of Speech Recognition. Prentice-Hall: Englewood Cliffs, NJ, 1993.
13. Plannerer B. An Introduction to Speech Recognition, Technical Report, Uniersity of Munich, Germany, 2003.
14. Sigurdsson S et al. Mel frequency cepstral coefficients: an evaluation of robustness of MP3 encoded music. ISMIR Conference Proceedings, Victoria, Canada, 2006.