  This study proposes a framework for the automated hyperparameter optimization
of a bearing fault detection pipeline for permanent magnet synchronous motors
(PMSMs) without the need of external sensors. A automated machine learning
(AutoML) pipeline search is performed by means of a genetic optimization to
reduce human induced bias due to inappropriate parameterizations. For this
purpose, a search space is defined, which includes general methods of signal
processing and manipulation as well as methods tailored to the respective task
and domain. The proposed framework is evaluated on the bearing fault detection
use case under real world conditions. Considerations on the generalization of
the deployed fault detection pipelines are also taken into account. Likewise,
attention was paid to experimental studies for evaluations of the robustness of
the fault detection pipeline to variations of the motors working condition
parameters between the training and test domain. The present work contributes
to the research of fault detection on rotating machinery in the following
terms: (1) Reduction of the human induced bias to the data science process,
while still considering expert and task related knowledge, ending in a generic
search approach (2) tackling the bearing fault detection task without the need
for external sensors (sensorless) (3) learning a domain robust fault detection
pipeline applicable to varying motor operating parameters without the need of
re-parameterizations or fine-tuning (4) investigations on working condition
discrepancies with an excessive degree to determine the pipeline limitations
regarding the abstraction of the motor parameters and the pipeline
hyperparameters
