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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.15962 |
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| _version_ | 1866914692200398848 |
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| author | Verma, Ankur Oh, Seog-Chan Arinez, Jorge Kumara, Soundar |
| author_facet | Verma, Ankur Oh, Seog-Chan Arinez, Jorge Kumara, Soundar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15962 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing Verma, Ankur Oh, Seog-Chan Arinez, Jorge Kumara, Soundar Machine Learning 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. |
| title | Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.15962 |