Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing

arXiv:2402.15962v1 Announce Type: new
Abstract: Manufacturing energy consumption data contains important process signatures required for operational visibility and diagnostics. These signatures may be of different temporal scales, ranging from monthly to sub-second resolutions. We introduce a hierarchical machine learning approach to identify automotive process signatures from paint shop electricity consumption data at varying temporal scales (weekly and daily). A Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and Principal Component Analysis (PCA) combined with Logistic Regression (LR) are used for …

abstract arxiv automotive consumption cs.lg data diagnostics energy hierarchical identify machine machine learning manufacturing process temporal type visibility