# Online Fault ID

We use M-ary Bayesian Hypothesis Testing to verify nominal system performance (or lack thereof) in real-time.

We developed a Model-Based Fault Identification (MBFID) algorithm that utilizes a technique called M-ary Bayesian Hypothesis Testing. Our MBFID framework requires telemetry data from a digital-twin capable of accurately simulating the real autonomous vehicle’s nominal telemetry and the effects of known faults. Given this, our MBFID framework can detect many different faults with over 95% success rate. Since this project was funded by the Air Force Research Laboratory (AFRL) in collaboration with Verus Research, the experiments and project evolution were spacecraft-specific. However, our methodologies remain generalizable to any dynamical system.

Intuitively, we run multiple estimators (e.g. a Kalman Filter) simultaneously, each one assuming (via the state and measurement equations) a particular operation mode/fault. Then, given a measurement from the real system, the M-ary Bayesian Hypothesis Testing framework creates an M-partition of the entire observation space and fits a posterior distribtuion given the measurement. Then, the tester can select the mode that the system is *most likely* in. We also can effectively identify unknown anomalies this way by recognizing when the system is most likely **not** in any modeled mode.

We have shown that that this method is very effective at identifying both known and unknown faults. Some examples are shown below.

I invite you to investigate any of these great resources to learn more about our work on this project:

- The implementation of the M-ary Bayesian Hypothesis Tester