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.

制造业能源消耗数据中包含了操作可见性和诊断所需的重要过程特征。本研究通过引入一种层次化机器学习方法，从不同时间尺度（每周和每日）的涂装厂用电数据中识别汽车生产过程特征。我们使用多层感知机（MLP）、卷积神经网络（CNN）以及主成分分析（PCA）结合逻辑回归（LR）进行分析。验证了所开发算法在操作可见性和能源节约机会识别方面的实用性。