{"title":"电池生产中混合过程的正常行为建模","authors":"Antje Fitzner , Thomas Ackermann , Melanie Lübke","doi":"10.1016/j.procir.2025.02.171","DOIUrl":null,"url":null,"abstract":"<div><div>The rising global demand for lithium-ion battery cells (LIBs) underscores the need for efficient production processes. Predictive maintenance (PdM) plays a crucial role by transforming data into insights, enhancing equipment reliability. Normal Behavior Modelling (NBM) enables anomaly detection, aiming to improve operational efficiency and product quality in this complex manufacturing environment.</div><div>An NBM of the extruder is developed using real production data for the electrode mixing process. The model captures the normal behavior of the machine by predicting the pressure as a measure of the system state of the machine. Identifed anomalies are highlighted and can later be used for a full PdM. The results will enhance the monitoring of the LIB production line and prevent unintended downtimes of machines.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 520-525"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Normal Behavior Modelling for the Mixing Process in Battery Cell Production\",\"authors\":\"Antje Fitzner , Thomas Ackermann , Melanie Lübke\",\"doi\":\"10.1016/j.procir.2025.02.171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rising global demand for lithium-ion battery cells (LIBs) underscores the need for efficient production processes. Predictive maintenance (PdM) plays a crucial role by transforming data into insights, enhancing equipment reliability. Normal Behavior Modelling (NBM) enables anomaly detection, aiming to improve operational efficiency and product quality in this complex manufacturing environment.</div><div>An NBM of the extruder is developed using real production data for the electrode mixing process. The model captures the normal behavior of the machine by predicting the pressure as a measure of the system state of the machine. Identifed anomalies are highlighted and can later be used for a full PdM. The results will enhance the monitoring of the LIB production line and prevent unintended downtimes of machines.</div></div>\",\"PeriodicalId\":20535,\"journal\":{\"name\":\"Procedia CIRP\",\"volume\":\"134 \",\"pages\":\"Pages 520-525\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Procedia CIRP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212827125005372\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827125005372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Normal Behavior Modelling for the Mixing Process in Battery Cell Production
The rising global demand for lithium-ion battery cells (LIBs) underscores the need for efficient production processes. Predictive maintenance (PdM) plays a crucial role by transforming data into insights, enhancing equipment reliability. Normal Behavior Modelling (NBM) enables anomaly detection, aiming to improve operational efficiency and product quality in this complex manufacturing environment.
An NBM of the extruder is developed using real production data for the electrode mixing process. The model captures the normal behavior of the machine by predicting the pressure as a measure of the system state of the machine. Identifed anomalies are highlighted and can later be used for a full PdM. The results will enhance the monitoring of the LIB production line and prevent unintended downtimes of machines.
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