Indicator of Alarm Risk on Product Degradation, Prediction for Alarms Grouping, Using Alarms Data in Semiconductor Manufacturing
| Autor: | Mohammed Al-kharaz, Bouchra Ananou, Jacques Pinaton, Mustapha Ouladsine, Michel Combal |
|---|---|
| Přispěvatelé: | Pronostic-Diagnostic Et CommAnde : Santé et Energie (PECASE), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), STMicroelectronics, Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry media_common.quotation_subject 020208 electrical & electronic engineering 02 engineering and technology Automation Reliability engineering Product (business) ALARM Identification (information) 020901 industrial engineering & automation [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering 0202 electrical engineering electronic engineering information engineering Production (economics) Quality (business) business media_common |
| Zdroj: | 58th Conference on Decision and Control (CDC) 58th Conference on Decision and Control (CDC), Dec 2019, Nice, France CDC |
| Popis: | International audience; The performance of industrial alarm systems has become a subject of very high interest as they strongly contribute to avoiding undesired or abnormal situations during production operation. They are considered as a fundamental part of any production facility and their efficiencies are certainly influencing the final products’ quality. Therefore, the increase in process equipment and automation degree have raised, as a consequence, the number of configured alarms to monitor the processes, which, during the operation, results in floods of alarms that decreased the effectiveness of alarm system and increased operator workloads beyond their capacities. The identification of critical and relevant alarms to products quality helps in monitoring simultaneously alarm performance and their impact on the final product. This paper presents an approach based on the AdaBoost algorithm for addressing alarm issues by predicting their risk of final product degradation as a function of their statistical behaviors of activation on product lots during production operation which in turns has used to group alarms. The results show a good performance of this method which has demonstrated on a real dataset collected from a semiconductor fabrication facility. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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