| Project/Area Number |
10650410
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Kobe University |
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Principal Investigator |
KOTANI Manabu Fac. of Eng., Kobe University, Associate Professor, 工学部, 助教授 (30215272)
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| Co-Investigator(Kenkyū-buntansha) |
OZAWA Seiichi Grad. Sch. of Sci. and Tech., Kobe University, Associate Professor, 自然科学研究科, 助教授 (70214129)
OGAWA Kazuhiko Fac. of Eng., Kobe University, Associate Professor, 工学部, 助教授 (30252802)
AKAZAWA Kenzo Fac. of Eng., Kobe University, Professor, 工学部, 教授 (30029277)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 1999: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1998: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | acoustics / machine condition diagnosis technique / neural networks / gas leak / pipe / module / ガウス関数 / 音響法 |
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| Research Abstract |
The detection of gas leakage sound from pipes is important in petroleum refining plants and chemical plants, as often the gas used in these plants are flammable or poisonous. In order to establish the acoustic diagnosis technique for the leakage sound, we examined the application of modular neural networks to the stable detection. The modular neural network has the ability to adapt its structure according to the environment. Experiments were performed for an artificial gas leakage device with various experimental conditions to imitate the change of environment for a long term. We applied Fast Fourier Transform(FFT) as the pre-processing method and examine features of power spectrum for the gas leakage sound. The feature is that the power spectrum for the gas leakage sound are more than those for the normal sound within the range from about 5kHz to 20kHz. The discrimination accuracy with the proposed network was observed to be about 93%. From the results, we confirmed the effectiveness for the application of the modular neural network to the detection of the leakage sound for the practical use. Furthermore, we have developed the handy system based on the diagnostic technique.
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