Acoustic Resonance Testing of Small Data on Sintered Cogwheels

Abstract

Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.

A Real-Time Application for the Analysis of Multi-Purpose Vending Machines with Machine Learning

“…Collecting data and labeling them correctly is time-consuming and labor-intensive, and for some real-world situations, one has to deal with small data. In recent years, many researchers have attempted to apply machine learning to small data [ 18 , 19 , 20 , 21 ]. Despite the fact that there is much literature related to unmanned retail and smart vending machines, there is a complete lack of literature on dealing with the problem of products getting stuck to the best of our knowledge.…”