Efficient Sensor Fault Detection Using Combinatorial Group Testing

Chun Lo, Mingyan Liu, Jerome P. Lynch, Anna C. Gilbert

Abstract: This paper introduces a novel use of concepts from combinatorial group testing and Kalman filtering in detecting faulty sensors in a network when faults are relatively rare. By assigning sensors to specific groups and performing Kalman filter-based fault detection over these groups, we can obtain a small binary detection outcome, which can be decoded to reveal the fault state of all sensors in the network. Compared to existing methods, our algorithm achieves similar or better detection accuracy with fewer tests and thus lower computational complexity. We perform extensive numerical analysis using a set of real vibration data collected from the New Carquinez Bridge in California using an 18-sensor network mounted on the bridge.