Detecting external measurement disturbances based on statistical analysis for smart sensors

IEEE International Symposium on Industrial Electronics

Volumn , Issue , 2010, Pages 2067-2072

  Dietrich, André ^a   Zug, Sebastian ^a   Kaiser, Jörg ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Disturbed sensor measurements; Fault detection; Non parametric statistics; Smart sensor

Indexed keywords

CURRENT SENSING; ENVIRONMENTAL CONDITIONS; EXTERNAL DISTURBANCES; FAULT DETECTION ALGORITHM; FAULTY MEASUREMENTS; INFRA-RED SENSOR; MEASUREMENT DISTURBANCES; MEASUREMENT PROCEDURES; MEASUREMENT RESULTS; NON-PARAMETRIC STATISTICS; SENSOR MEASUREMENTS; SMART SENSING; STATISTICAL ANALYSIS; STATISTICAL APPROACH; VALIDITY ESTIMATION;

FAULT DETECTION; INDUSTRIAL ELECTRONICS; SENSOR ARRAYS; SMART SENSORS;

MEASUREMENTS;

EID: 78650368677     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIE.2010.5637754     Document Type: Conference Paper

References (18)

1. R. Breckenridge and S. Katzberg, "Smart sensors for the 80's-The status of smart sensors," in Sensor Systems for the 80's Conference, Colorado Springs, Colo, 1980, pp. 41-46.
2. S. Zug and J. Kaiser, "An approach towards smart fault-tolerant sensors," in Procedings of the International Workshop on Robotics and Sensors Environments (ROSE 2009), Lecco, Italy, November 2009.
3. Wenglor Sensoric, Data sheet YT87MGV80, 2003. [Online]. Available: http://www.q-tech.hu/pdf/Wenglor/Photoelectric20sensors/YT87MGV80.pdf
4. V. Nelson, "Fault-tolerant computing: Fundamental concepts," Computer, vol. 23, no. 7, pp. 19-25, 1990.
5. B. Hardekopf, K. Kwiat, and S. Upadhyaya, "Secure and fault-tolerant voting in distributed systems," in Aerospace Conference, 2001, IEEE Proceedings., vol. 3, 2001.
6. R. Isermann, "Model-based fault-detection and diagnosis-status and applications," Annual Reviews in control, vol. 29, no. 1, pp. 71-85, 2005.
7. P. Frank and B. Köppen-Seliger, "Fuzzy logic and neural network applications to fault diagnosis," International Journal of Approximate Reasoning, vol. 16, no. 1, pp. 67-88, 1997.
8. M. Basseville and I. Nikiforov, Detection of abrupt changes: theory and application. Citeseer, 1993.
9. R. Isermann, Fault-diagnosis systems. Springer, 2006.
10. M. Niedzwiecki and K. Cisowski, "Adaptive scheme for elimination of broadband noise and impulsivedisturbances from AR and ARMA signals," IEEE Transactions on Signal Processing, vol. 44, no. 3, pp. 528-537, 1996.
11. Z. Peng and F. Chu, "Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography," Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199-221, 2004.
12. D. Sheskin, Handbook of parametric and nonparametric statistical procedures, 3rd ed. Chapman & Hall/CRC, Aug. 2003.
13. M. J. Crawley, The R Book, 1st ed. Wiley & Sons, Apr. 2007.
14. Sharp Cooperation, GP2Y0A02YK Data Sheet, 2007. [Online]. Available: http://sharp-world.com/products/device/lineup/data/pdf/datasheet/gp2y0a21yke.pdf
15. National Instruments, NI DAQCard-6036E Data Sheet, 2005. [Online]. Available: http://sine.ni.com/nips/cds/print/p/lang/de/nid/11914
16. SciPy Project Page, 2009. [Online]. Available: http://www.scipy.org
17. R. Gentleman, V. Carey, W. Huber, S. Dudoit, and R. Irizarry, Bioinfor-matics and computational biology solutions using R and Bioconductor. Springer Verlag, Sep. 2005.
18. C. Goutte and E. Gaussier, "A probabilistic interpretation of precision, recall and f-score, with implication for evaluation," in Proceedings of the 27th European Conference on Information Retrieval, 2005, pp. 345-359.