Assessment of Feature Selection Algorithms for Knowledge Discovery from Experimental Data

Maintenance and repair play a crucial role in industry. Smart systems for technical diagnostics can help to save money and to prevent the breakdown of machines and plants. These systems and its classifiers benefit from plausible features because they tend toward robust classification. Although concepts for knowledge discovery are well-known in various scientific fields, they are not established in the field of rotating machines. Knowledge discovery from experimental data is a framework that combines valid methods for knowledge discovery with expert knowledge and automated experiments. For the central data mining step, feature selection algorithms based on heuristic or meta-heuristic search are established. The objective is to identify plausible pattern with a limited number of features and the best combination of these features. The results in this work show which strategies align the best with the requirements of knowledge discovery using experimental data to find plausible features. For this study, well-configured search strategies, namely, sequential forward selection and ant colony optimization, were applied on real data. The data represent several fault severity levels for parallel misalignment and cavitation. The plausible feature vectors and features exhibited good behavior when applied to new targets. It is expected that the obtained knowledge will be transferable to new classification tasks with only minimal optimization of the reference data or the classifier.