Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach

TL;DR

This paper introduces a real-time, onboard fault detection method for quadrotors using a Kalman filter to estimate actuator effectiveness, enabling quick identification of failures with minimal model dependence.

Contribution

It proposes a novel, low-latency fault detection algorithm that operates online with minimal model assumptions, validated through real flight data and integrated into a fault-tolerant control system.

Findings

Detection delay between 30 to 130 ms

No missed detections or false alarms in tests

Validated with real flight data including propeller ejections

Abstract

This paper presents a novel method for fast and robust detection of actuator failures on quadrotors. The proposed algorithm has very little model dependency. A Kalman filter estimator estimates a stochastic effectiveness factor for every actuator, using only onboard RPM, gyro and accelerometer measurements. Then, a hypothesis test identifies the failed actuator. This algorithm is validated online in real-time, also as part of an active fault tolerant control system. Loss of actuator effectiveness is induced by ejecting the propellers from the motors. The robustness of this algorithm is further investigated offline over a range of parameter settings by replaying real flight data containing 26 propeller ejections. The detection delays are found to be in the 30 to 130 ms range, without missed detections or false alarms occurring.