Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5
Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available mult...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/7/12/175 |
| _version_ | 1827641176326406144 |
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| author | Tobias Schlagenhauf Jan Wolf Alexander Puchta |
| author_facet | Tobias Schlagenhauf Jan Wolf Alexander Puchta |
| author_sort | Tobias Schlagenhauf |
| collection | DOAJ |
| description | Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning. |
| first_indexed | 2024-03-09T17:09:37Z |
| format | Article |
| id | doaj.art-a9d7ecd82b094017befb5ef8f58e7cac |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T17:09:37Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-a9d7ecd82b094017befb5ef8f58e7cac2023-11-24T14:13:53ZengMDPI AGData2306-57292022-12-0171217510.3390/data7120175Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5Tobias Schlagenhauf0Jan Wolf1Alexander Puchta2WBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, GermanyWBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, GermanyWBK Institute for Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, GermanyMachine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning.https://www.mdpi.com/2306-5729/7/12/175time seriesmachine learninganomaly detectiontransfer learning |
| spellingShingle | Tobias Schlagenhauf Jan Wolf Alexander Puchta Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 Data time series machine learning anomaly detection transfer learning |
| title | Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 |
| title_full | Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 |
| title_fullStr | Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 |
| title_full_unstemmed | Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 |
| title_short | Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5 |
| title_sort | convolutional based encoder decoder network for time series anomaly detection during the milling of 16mncr5 |
| topic | time series machine learning anomaly detection transfer learning |
| url | https://www.mdpi.com/2306-5729/7/12/175 |
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