Distributed Diagnosis of Electric Aircraft Powertrain



Published Jun 30, 2018
Chetan S Kulkarni Indranil Roychoudhury George Gorospe


Electrically powered autonomous aircraft are being increasingly considered for intra-city short-haul air-taxi services to fly human passengers between different locations. As a result, it is critical to incorporate safety under autonomous operations into system operations by enabling such autonomous aircraft to make accurate estimates of its current health state and take the right decisions to complete its mission successfully. The first step to assess health state of the entire aircraft is for the aircraft to be able to assess the health of its individual critical systems, (e.g electrical powertrain) for it to be able to fly and reach its destination in a safe manner. The fundamental components of a powertrain in an electrically powered aircraft include key electrical components such as batteries, motors, and power electronics (e.g., electronic speed controllers). A model-based diagnosis approach of complex critical systems enables their safe and efficient operation. Typically, such model-based schemes are centralized approaches that suffer from inherent disadvantages such as computational complexity, single point of failure, and scalability issues. Distributing the diagnosis task addresses these issues. To this end, this paper presents the results of implementing a distributed diagnostics approach to a representative electric aircraft powertrain. In particular, we focus on the implementation of a distributed diagnosis framework to a quadrotor vehicle. The simulation experiments demonstrate how the distributed diagnosis algorithm correctly and efficiently diagnose faults in the rotorcraft’s electric powertrain.

How to Cite

Kulkarni, C. S., Roychoudhury, I., & Gorospe, G. (2018). Distributed Diagnosis of Electric Aircraft Powertrain. PHM Society European Conference, 4(1). https://doi.org/10.36001/phme.2018.v4i1.559
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Quadcopter, Distributed Diagnosis, Electric Powertrain, Fault Detection

Technical Papers