A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

Sensors

Daniel Smith¹  Greg Timms¹  Paulo De Souza² 

[1] Intelligent Sensing and System Laboratory (ISSL), Commonwealth Science and Industrial Research Organisation (CSIRO), CSIRO Marine and Atmospheric Laboratories, Castray Esplanade, Hobart 7001, Australia;Human Interface Technology Laboratory, University of Tasmania, Launceston 7250, Australia

关键词: online filtering;    automated;    quality assessment;    sensors;    dynamic Bayesian networks;   

DOI  :  10.3390/s120709476

来源: mdpi

【 摘 要 】

Online automated quality assessment is critical to determine a sensor's fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state and observed effects of individual sensor errors without imposing any constraints upon the physical deployment or measured phenomenon. It represents the casual relationship between quality tests and combines them in a way to generate uncertainty estimates of samples. The DBN was implemented for a particular marine deployment of temperature and conductivity sensors in Hobart, Australia. The DBN was shown to offer a substantial average improvement (34%) in replicating the error bars that were generated by experts when compared to a fuzzy logic approach.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.