Study of Anti-Radiation Sensor Technology for Fault Diagnosis System of Nuclear Facilities

Abstract:

Due to the complex structure of nuclear facilities in a high irradiation environment, people were hard to approach it, In view of these situation a fault diagnosis method based on HMM(Hidden Markov Model) of capturing the audio signal while facilities were operating was proposed. With the strong modeling ability, HMM could be applied to analyzing such as audio signal non-stationary time signal. By using this method, the original mechanical structures of nuclear facilities were not destroyed. The preposed sensors were needed as few as possible by the whole diagnosis system and which had a simple structure, low cost structure, the fault diagnosis rate was high and so on. State monitoring and fault diagnosis system of complex nuclear ower equipment could timely and effective to provide running status and potential failure information for operating personnel, which had a vital significance for the safe and reliable operation of nuclear power equipment.

• Periodical: Advanced Materials Research (Volumes 889-890)
• Pages: 813-816
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.889-890.813
• Online since: February 2014
• Authors: Feng Wei Yuan*, Qian Deng, Yu Lin Wang
• Keywords: Fault Diagnosis, Hidden Markov Model (HMM), Nuclear Facilities

References

[1] Hasan OCAK, Kenneth A LOPARO. A New Bearing Fault Detection and Diagnosis Scheme Based on Hidden Markov Modeling of Vibration Signals. (IEEE Trans. on Aeousties, Speeeh, and Signal Proeessing. 2001). Vol. 5. Page 3141-3144.

DOI: 10.1109/icassp.2001.940324

[2] D. Munteanu. Modeling and Simulation of Single Event Effects in Digital Devices and ICs. (IEEE Trans. on Nuclear Science. 2008), Vol. 55(4). Page 1854~1877.

DOI: 10.1109/tns.2008.2000957

[3] G. R. Allen. Compendium of Test Results of Single Event Effects Conducted by the Jet Propulsion Laboratory. (IEEE Radiation Effects Data Workshop Record, 2008). Page 21~29.

DOI: 10.1109/redw.2008.11

[4] Bengio S: Information Fusion, Vol. 5 (2004) No. 2, pp.81-89.

[5] Werner S, Rigool G. Pseudo 2-Dimensional hidden Markov models in speech recognition. (IEEE Workshop on Automatic Speech Recognition and Understanding, 2001). Page 441-444.

DOI: 10.1109/asru.2001.1034679

[6] Y.K. Liu, H Xia, and C.L. Xie: Atomic Energy Science and Technology, Vol. 42 (2008) No. 3, p.200. (In Chinese).

[7] X.J. Ma, F.L. Wang, and Y. Cai: Atomic science and technology, Vol. 24 (2004) No. 3, p.161. (In Chinese).

[8] N.Y. Wang: research and development of Journal of world science and technology, Vol. 24 (2002) No. 6, p.14. (In Chinese).