Application Of Analysis Of Variance To Determine Important Features Of Signals For Diagnostic Classifiers Of Displacement Pumps

This paper discusses the use of one-way analysis of variance (ANOVA) as a tool for ranking features in diagnostic signals and evaluating their impact on the accuracy of machine learning systems in classifying displacement pump wear. The study includes a review of diagnostic systems, a description of typical pump damage and its causes, and a diagnostic experiment using time-frequency analysis. The results show that ANOVA can effectively rank features and improve the accuracy of pump wear evaluation.