An Approach to Condition Monitoring of BLDC Motors with Experimentally Validated Simulation Data

Abstract

Due to their compact design and low number of wear parts, Brushless Direct Current (BLDC) motors are ideally suited for use in unmanned aerial vehicles (UAVs). In view of the growing areas of application and the increasing complexity of unmanned flight missions, the need for suitable safety mechanisms for the operation of technical components, such as BLDC motors, in unmanned aircraft drive trains is also increasing. The integration of redundant components analogous to manned aviation is often not possible for smaller unmanned aerial vehicles for weight reasons. Therefore, online-capable dynamic diagnosis and prognosis methods for monitoring safety-critical components of unmanned aircraft are subject of ongoing research.

One major challenge in the development of data based condition monitoring approaches for safety critical components is the availability of operational data of degraded components. This often leads to an unbalanced database without sufficient information on components’ degradation behavior.

In the presented work, this problem is approached by combining bench testing and simulation models. On a test rig, common degradation effects are recreated by targeted manipulation. This allows for a safe and expressive data acquisition of the components’ behavior. In order to reduce the material and time required to build up a sufficient database for condition monitoring with experimental data, the observable effects are replicated in a simulation. This provides the opportunity to create a large database with slight variations in simulation parameters and incorporated noise in the simulation.

The BLDC motor manipulation on the test rig includes mechanical, electrical and magnetic manipulation. The effects of the manipulation are analyzed and their representation by parameters in the corresponding simulation is derived. The model is built in MATLAB Simulink and replicates both the electrical and physical behavior of the motor, as well as its commutation behavior.

The established simulation data shall be used as a balanced dataset on which condition monitoring algorithms can be trained. This will allow for the comparison of various data based condition monitoring methods in the future. A remaining challenge lies in the time behavior of the analyzed degradation, which has not yet been explored in depth. The proposed approach might also be applied to further unmanned aerial vehicle components, such as servo motors.