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A Framework for the Automated Parameterization of a Sensorless Bearing Fault Detection Pipeline
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
Biobjective Performance Assessment with the COCO Platform
This document details the rationales behind assessing the performance of numerical black-box optimizers on multi-objective problems within the COCO platform and in particular on the biobjective test suite bbob-biobj.
