Health monitoring and pre-flight diagnostics for robust electrical machines

Electrified powertrains have the potential to enable more economical, efficient, safe, and robust air vehicles. The electrical machine plays a central role in all of these topologies, whether configured as a generator or as a propulsive motor. Hinetics uses fault-tolerant design processes to limit the severity and likelihood of failures. However, once mounted to the vehicle, electrical machines are subjected to extreme environmental conditions and maintenance events with inherent uncertainty. To support the safety goals related to electrical machines, Hinetics proposes developing a health monitoring system and corresponding algorithm using multi-modal measurement sources, physics-based models, and advanced signal processing and machine learning techniques. The goal is to detect imminent failures and communicate them to the central controller or operator in order to take actions that limit the impact of the failure. In Phase I, Hinetics will: (1) tabulate an exhaustive list of failure modes, sources, and indicators in its slotless permanent magnet machine topology, (2) design and implement a sensor and data acquisition system, (3) characterize nominal and anomalous machine behavior for two key failure modes, and (4) develop and demonstrate the detection algorithm on its HITRACE testbed using one of Hinetics existing megawatt-class machines. Hinetics will also develop a pre-flight diagnostic routine to complete during taxi to detect failures prior to take-off. This work directly supports Hinetics’ commercialization plans by addressing key compliance requirements imposed by a potential airframer customer.