Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

Wen Hua Hu; Bao Feng Guo; Dong Fang Xue

doi:10.4028/www.scientific.net/AMM.347-350.387

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Fault Diagnosis Technology Research on Radar...

Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

Abstract:

Aiming at many fault characteristic parameters of modern radar and the problem that general diagnosis method is difficult to locate fault accurately. The dynamic mathematical model of radar equipment fault diagnosis system is built. The uncertainty theoretic analysis of fault diagnosis result is studied from two aspects, data processing channel and fault model. This paper combines sensor data validation with detection fusion technology, and then puts forward the fault diagnosis technology based on sensor data validation and detection fusion. The structure of the diagnostic system model is given. Case analysis proves that the proposed model and method has a significant role in improving detection performance, reducing the Bayes risk and improve fault diagnosis rate.

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Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

387-391

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.387

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Online since:

August 2013

Authors:

Wen Hua Hu, Bao Feng Guo, Dong Fang Xue

Keywords:

Fault Diagnosis, Information Fusion, Radar, Sensor Detection Fusion

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References

[1] Lv Feng. Research state and progress of fault diagnosis based on information fusion[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition), 2009. 37(8): 217-220.

Google Scholar

[2] Jing Tao. The Survey on Fault Diagnosis based on Information Fusion Technology [J]. Journal of Sichuan Ordnance, 2009. 30(7) : 127-129.

Google Scholar

[3] Yang Guang. Research on False-Alarm-Rate Decreasing at Sensor-Level of Built-in Test Systems in Mechatronic Products[D]. Changsha: National University of Defense Technology. (2003).

Google Scholar

[4] Misra A, Sztipanovits J. Model-Based Failure Detection, Isolation and Recovery System [C]. Proceedings of a Joint Conference, Mobile, Alabama, AD-A325558, 1996: 1-9.

Google Scholar

[5] Han Chongzhao. Multi-data Information Fusion [M]. Beijing: Tsinghua University Press. (2003).

Google Scholar