Articles | Volume 5, issue 2
https://doi.org/10.5194/jsss-5-337-2016
Special issue:
https://doi.org/10.5194/jsss-5-337-2016
Regular research article
 | 
18 Oct 2016
Regular research article |  | 18 Oct 2016

Sensor defect detection in multisensor information fusion

Jan-Friedrich Ehlenbröker, Uwe Mönks, and Volker Lohweg

Cited articles

Alpaydın, E.: Introduction to Machine Learning, Adaptive computation and machine learning, MIT Press, Cambridge, MA, 2nd Edn., 2010.
Bay, S. D. and Schwabacher, M.: Mining distance-based outliers in near linear time with randomization and a simple pruning rule, in: The Ninth ACM SIGKDD International Conference, edited by: Senator, T., Domingos, P., Faloutsos, C., and Getoor, L., p. 29, https://doi.org/10.1145/956750.956758, 2003.
Bishop, C. M.: Pattern recognition and machine learning, Information science and statistics, Springer, New York, NY, 8th Edn., 2009.
Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., and Lee, I.-B.: Fault detection and identification of nonlinear processes based on kernel PCA, Chemometr. Intell. Lab., 75, 55–67, https://doi.org/10.1016/j.chemolab.2004.05.001, 2005.
Dempster, A. P.: Upper and lower probabilities induced by a multivalued mapping, Ann. Math. Stat., 38, 325–339, https://doi.org/10.1214/aoms/1177698950, 1967.
Download
Short summary
This paper presents a novel method for the detection of sensor defects. Here, the consistency between measurements of sensor groups are utilized for this method. The sensor groups are pre-determined by the structure of an existing sensor fusion algorithm, which is in turn used to determine the health of a monitored system (e.g. a machine). Defect detection results of the presented method for different test cases and the method's capability to detect a number of typical sensor defects are shown.
Share
Special issue