Canonical correlation analysis framework for the reduction of test time in industrial manufacturing quality tests

Abstract

In industrial manufacturing processes, quality control tests are performed in order to measure product characteristics which help assess and classify the product’s quality. In this work, we focus on quality tests during which a signal is recorded for each product. We propose the usage of data-driven methods for a potential reduction of the test duration, without inducing loss in the quality classification performance. While in industrial practice most features extracted from the signals are still often hand crafted by domain experts and used as input to shallow classifiers, more advanced classification methods such as Convolutional Neural Networks (CNNs) are able to combine the feature extraction, selection and classification into a single process. In this paper we first use CNNs to determine whether an excerpt of the recorded signal exists which, starting at time 0, contains already enough information so as to match the classification performance reached with the usage of the complete signal. Second, we apply the Canonical Correlation Analysis (CCA) framework to investigate how the features extracted and selected from multiple, successively increasing excerpts relate to the features the quality test was originally designed to measure. Third, we analyze the presence of noise among the classification-relevant features extracted from the increasing excerpts. The suitability of the proposed framework is validated using a real-world dataset from the automotive industry, showing that the test time of the corresponding vibroacoustical quality test can be reduced by 77.78%, thus ensuring a high practical relevance of the findings.