Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing
This work addresses energy efficiency and diagnostics in automotive manufacturing, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of identifying process signatures from manufacturing energy consumption data at varying temporal scales, using a hierarchical machine learning approach on automotive paint shop electricity data, and validated it for operational visibility and energy savings with subject matter experts.
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 the analysis. We validate the utility of the developed algorithms with subject matter experts for (i) better operational visibility, and (ii) identifying energy saving opportunities.