Particle-Filter-Enabled Real-Time Sensor Fault Detection Without a Model of Faults

TL;DR

This paper introduces a particle filter-based method for real-time sensor fault detection that does not require prior knowledge of fault models, effectively identifying faulty measurements and enhancing state estimation accuracy.

Contribution

The authors propose a novel fault detection approach leveraging particle filters that operates without any predefined fault models, improving robustness in sensor validation.

Findings

Correctly identifies nearly 90% of faulty measurements

Achieves only a 3% increase in estimation error compared to perfect detection

Demonstrates effectiveness in nonlinear vehicle traffic models

Abstract

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 demonstrating its ability to reject faulty measurements and improve state estimation accuracy in 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.