Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (elektronisch) | 9781665497947 |
| ISBN (Print) | 978-1-6654-9795-4 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022 - Lisbon, Portugal Dauer: 13 Juli 2022 → 15 Juli 2022 |
Publikationsreihe
| Name | International Conference on Control, Automation and Diagnosis |
|---|---|
| ISSN (Print) | 2767-987X |
| ISSN (elektronisch) | 2767-9896 |
Abstract
Fault detection for a multi-stage manufacturing process is often challenging due to the lack of quality inspection after each individual stage. In most cases, the final product is rated by an end-of-process quality inspection. This leads to a difficult identification of the manufacturing stage in question. This paper presents a novel approach for fault detection of a multi-stage manufacturing process using machine learning. For this approach, an autoregressive model is used, which is enhanced by a neural network to create a residual between process measurements and model predictions. The residual is then evaluated to detect a fault in an individual manufacturing stage and in the experimental study a True Positive Rate of 0.79 is reached for a False Positive Rate of 0.07. The major advantage of the proposed approach is the detection of the fault without an explicit quality inspection for each individual stage.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Mathematik (insg.)
- Steuerung und Optimierung
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Angewandte Informatik
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- RIS
2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022. Institute of Electrical and Electronics Engineers Inc., 2022. ( International Conference on Control, Automation and Diagnosis).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Fault Detection in Multi-stage Manufacturing to Improve Process Quality
AU - Kellermann, Christoph
AU - Selmi, Ayoub
AU - Brown, Dominic
AU - Ostermann, Joern
N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany, within the framework of the IIP-Ecosphere project.
PY - 2022
Y1 - 2022
N2 - Fault detection for a multi-stage manufacturing process is often challenging due to the lack of quality inspection after each individual stage. In most cases, the final product is rated by an end-of-process quality inspection. This leads to a difficult identification of the manufacturing stage in question. This paper presents a novel approach for fault detection of a multi-stage manufacturing process using machine learning. For this approach, an autoregressive model is used, which is enhanced by a neural network to create a residual between process measurements and model predictions. The residual is then evaluated to detect a fault in an individual manufacturing stage and in the experimental study a True Positive Rate of 0.79 is reached for a False Positive Rate of 0.07. The major advantage of the proposed approach is the detection of the fault without an explicit quality inspection for each individual stage.
AB - Fault detection for a multi-stage manufacturing process is often challenging due to the lack of quality inspection after each individual stage. In most cases, the final product is rated by an end-of-process quality inspection. This leads to a difficult identification of the manufacturing stage in question. This paper presents a novel approach for fault detection of a multi-stage manufacturing process using machine learning. For this approach, an autoregressive model is used, which is enhanced by a neural network to create a residual between process measurements and model predictions. The residual is then evaluated to detect a fault in an individual manufacturing stage and in the experimental study a True Positive Rate of 0.79 is reached for a False Positive Rate of 0.07. The major advantage of the proposed approach is the detection of the fault without an explicit quality inspection for each individual stage.
KW - autoregression
KW - fault detection
KW - multi-stage manufacturing process
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85137829685&partnerID=8YFLogxK
U2 - 10.1109/ICCAD55197.2022.9853909
DO - 10.1109/ICCAD55197.2022.9853909
M3 - Conference contribution
AN - SCOPUS:85137829685
SN - 978-1-6654-9795-4
T3 - International Conference on Control, Automation and Diagnosis
BT - 2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022
Y2 - 13 July 2022 through 15 July 2022
ER -