A self-contained automated anomaly detection system for unmanned systems is presented and evaluated. The system and software design provides a platform-agnostic systems health monitoring solution in a small embedded sensing and processing package. Algorithms capable of learning normal operating conditions and detecting anomalies are coupled with a low size, weight, and power hardware implementation to provide a non-invasive, standalone health monitoring capability. Acoustic and inertial sensors (microphone, accelerometer and gyros) were selected as initial non-invasive sensors for detecting a range of common and costly mechanical faults. Test cases are shown to illustrate and validate the system usage and performance.

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