English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We are experiencing an explosion in the amount of sensors measuring our activities and the world around us. These sensors are spread throughout the built environment and can help us perform state estimation and control of related systems, but they are often built and/or maintained by third parties or system users. As a result, by outsourcing system measurement to third parties, the controller must accept their measurements without being able to directly verify the sensors' correct operation. Instead, detection and rejection of measurements from faulty sensors must be done with the raw data only. Towards this goal, we present a method of detecting possibly faulty behavior of sensors. The method does not require that the control designer have any model of faulty sensor behavior. As we discuss, it turns out that the widely-used particle filter state estimation algorithm provides the ingredients necessary for a hypothesis test against all ranges of correct operating behavior, obviating the need for a fault model to compare measurements. We demonstrate the applicability of our method by showing its ability to reject faulty measurements and accuracy in state estimation of a nonlinear vehicle traffic model, without information of generated faulty measurements' characteristics. In our test, we correctly identify nearly 90% of measurements as faulty or non-faulty without having any fault model. This leads to only a 3% increase in state estimation error over a theoretical 100%-accurate fault detector.

Particle-filter-enabled real-time sensor fault detection without a model of faults