Sensor fusion in integrated circuit fault diagnosis using a belief function model

International Journal of Distributed Sensor Networks

Volumn 4, Issue 3, 2008, Pages 247-261

  Zhu, Daqi ^a   Gu, Wei ^a  

^a SHANGHAI MARITIME UNIVERSITY   (China)

Author keywords

Belief Function; D S Evidence Theory; Fault Diagnosis; Information Fusion

Indexed keywords

CIRCUIT THEORY; DATA FUSION; ELECTRIC FAULT CURRENTS; ELECTRONICS INDUSTRY; FUSION REACTIONS; INFORMATION FUSION; INTEGRATED CIRCUITS; KETONES; NUCLEAR PHYSICS; SENSORS;

BELIEF FUNCTION; CIRCUIT COMPONENTS; D-S EVIDENCE THEORY; DEMPSTER-SHAFER; EVIDENCE THEORY; FAULT COMPONENTS; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; FUSION METHODS; INFORMATION FUSION METHOD; SENSOR FUSION;

SENSOR DATA FUSION;

EID: 48849083240     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1080/15501320701260626     Document Type: Article

References (22)

1. th Anniversary Conf. Advanced Research in VLSI, 1999, pp. 327-341.
2. Y. Q. Chen, "Experiment on fault location in large-scale analogue circuits," IEEE Trans. on IM. 1993, vol. 42, no. 1, pp. 30-34.
3. E. Waltz, D., Buede, "Data fusion and decision support for command and control," IEEE Transactions on SMC, 1986, vol. 16, no. 5, pp. 865-879.
4. Philip L. B., Shafer-Dempster reasoning with applications to multisensor target identification system, IEEE Transactions on SMC, 1987, Vol. 17, No. 6, pp. 968-977.
5. Luo R. C. and Scherp R. S., Dynamic multi-sensor data fusion system for intelligent robots. IEEE J. Robotics and Automation, 1998, Vol. 4, No. 4, pp. 386-396.
6. Gao P., Tantum S. and Collins L., Single sensor processing and sensor fusion of GPR and EMI data for landmine detection, Proc. SPIE Conf. Detection Technologies Mines Mine-like Targets, Orlando, 1999, Vol. 3710, pp. 1139-1148.
7. Gao. P., Tantum S. and Collins L., Single sensor processing and sensor fusion of GPR and EMI data for landmine detection, in Proc. SPIE Conference Detection Technologies Mines Minelike Targets, 1999, Vol. 3710, pp. 1139-1148, Orlando.
8. Bloch I., Information combination operators for data fusion: A comparative review with classification, IEEE Transactions on SMC, 1996, Vol. 26, No. 1, pp. 52-67.
9. Milisavljevic N. and Bloch I., Sensor fusion in anti-personal mine detection using a two-level belief function model, IEEE Trans. On Systems, Man, and Cybernetics, 2003, Vol. 33, No. 2, pp. 269-283.
10. Bienvenu F. M. and Jean B. C., A new probabilistic and entropy fusion approach for management of information sources, Information Fusion, 2004, Vol. 5, No. 1, pp. 35-47
11. Gail A. C., Siegfried M. and Ogi J. O., Self-organizing information fusion and hierarchical knowledge discovery: a new framework using ARTMAP neural networks, Neural Networks, 2005, Vol. 18, No. 3, pp. 287-295.
12. Deng Z. L., Gao Y., Mao L., and Hao G., New approach to information fusion steady-state Kalman filtering, Automatica, 2005, Vol. 41, No. 10, pp. 1695-1707
13. Zhang Y., Sun O., Jin Y.G. and Quan T., Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning, Journal of Systems Engineering and Electronics, 2006, Vol. 17, No. 3, pp. 495-501.
14. Venu D., Reinhold C. M. and Vladimir A. P., Information fusion for text classification - an experimental comparison, Pattern Recognition, 2001, Vol. 34, No. 12, pp. 2413-2425.
15. Jin X. Y. and Curt H. D., An integrated system for automatic road mapping from highresolution multi-spectral satellite imagery by information fusion, Information Fusion, 2005, Vol. 6, No. 4, pp. 257-273.
16. Wark T.and Sridharan S., Adaptive Fusion of Speech and Lip Information for Robust Speaker Identification, Digital Signal Processing, 2001, Vol. 11, No. 3, pp. 169-186.
17. Shu-Li Sun, Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor, Automatica, 2004, Vol. 40, No. 8, pp. 1447-1453.
18. Belur V. D., Information Fusion in the context of Biomedical Applications, Information Fusion, 2002, Vol. 3, No. 2, pp. 89-95.
19. Arun Ross and Anil Jain, Information fusion in biometrics, Pattern Recognition Letters, 2003, Vol. 24, No. 13, pp. 2115-2125
20. Dempster A., Upper and lower probabilities induced by multi-valued mapping, Ann. Math. Statist., 1967, Vol. 38, pp. 325-339.
21. Shafer G. A. Mathematical Theory of Evidence. Princeton: Princeton University Press, 1976.
22. Zhu D. Q. and Yu S. L., The studies of electronic component fault diagnosis based on the fault trees analysis and virtual instrument, Journal 0f Scientific Instrument, 2002, Vol. 16, No. 1, pp. 16-19.