Hierarchical energy signatures using machine learning for operational
  visibility and diagnostics in automotive manufacturing
Ankur Verma Seog-Chan Oh Jorge Arinez Soundar Kumara
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.