Biegel, Tobias ; Jourdan, Nicolas ; Madreiter, Theresa ; Kohl, Linus ; Fahle, Simon ; Ansari, Fazel ; Kuhlenkötter, Bernd ; Metternich, Joachim (2022)
Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing.
doi: 10.2139/ssrn.4073942
Conference or Workshop Item, Bibliographie
Abstract
One of the major challenges of today's manufacturing industry is the reliable detection of process anomalies and failures in order to reduce unplanned downtimes and avoid quality issues. Process Monitoring (PM) requires the existence of a Normal Operating Condition (NOC) dataset that is used to train the respective algorithm. Obtaining such a NOC dataset involves extensive test runs aside from the actual production. Machine operators often collect a variety of unstructured process specific data in form of protocols, that contain valuable information about the process condition. We propose an approach that utilizes such text data to efficiently create the NOC dataset for a machining process in one of our learning factories. Using the NOC high-frequency machine sensor readings, we train a principal component analysis (PCA)-based model, which can identify anomalous process behavior. The model is consequently evaluated on a holdout test data set and shows promising results. Estimations of the process condition are visualized with two control charts allowing intuitive insights for the machine operator.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2022 |
| Creators: | Biegel, Tobias ; Jourdan, Nicolas ; Madreiter, Theresa ; Kohl, Linus ; Fahle, Simon ; Ansari, Fazel ; Kuhlenkötter, Bernd ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing |
| Language: | English |
| Date: | 2022 |
| Place of Publication: | Rochester, NY |
| Publisher: | SSRN by Elsevier B.V. |
| Book Title: | Proceedings of the 12th Conference on Learning Factories (CLF 2022) |
| Series: | SSRN elibrary |
| Collation: | 6 Seiten |
| DOI: | 10.2139/ssrn.4073942 |
| Abstract: | One of the major challenges of today's manufacturing industry is the reliable detection of process anomalies and failures in order to reduce unplanned downtimes and avoid quality issues. Process Monitoring (PM) requires the existence of a Normal Operating Condition (NOC) dataset that is used to train the respective algorithm. Obtaining such a NOC dataset involves extensive test runs aside from the actual production. Machine operators often collect a variety of unstructured process specific data in form of protocols, that contain valuable information about the process condition. We propose an approach that utilizes such text data to efficiently create the NOC dataset for a machining process in one of our learning factories. Using the NOC high-frequency machine sensor readings, we train a principal component analysis (PCA)-based model, which can identify anomalous process behavior. The model is consequently evaluated on a holdout test data set and shows promising results. Estimations of the process condition are visualized with two control charts allowing intuitive insights for the machine operator. |
| Uncontrolled Keywords: | Process Monitoring, Text Mining, Anomaly Detection, MSPC |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > CiP Center for industrial Productivity |
| Date Deposited: | 23 Feb 2023 06:23 |
| Last Modified: | 23 Feb 2023 06:40 |
| PPN: | 505260972 |
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