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Oliver Wu(吳文斌)
Sr. Dept. Manager
Marketech International Corp. |
Education
- Master of Institute of Information Engineering, Cheng-Chung University
Experience
- 2010 ~ present, Sr. Dept. Manager, Marketech International Corp.
- Over 20 years of experience in CIM and automation
- Smart Manufacturing: providing software and hardware integration solutions in the semiconductor, electronics manufacturing, automotive components, precision chemical materials and beverage industries.
Topic: Artificial Intelligence Solutions for High-Tech Factory
Abstract
Regular maintenance of equipment was widely used. The equipment must be shut down for maintenance that according to the time of use or the number of uses after reaching a pre-defined value. As a result, regular downtime maintenance causes capacity Loss and regular replacement of components causes the cost increasing are a major problem. At the same time, the factory must take the risk of unanticipated equipment failure between the maintenance of the equipment and the next maintenance. Such as, emergency repairs result in increased maintenance time, reduced the efficiency of the production line, affected the product yield, and increased the workload of the duty officer.
MIC uses the technology of big data analysis and the machine learning method of artificial intelligence to construct equipment prognostics and health management (PHM) system that ability to provide equipment health assessment and remaining useful life predictions, and about 48 hours early, issue alarm to the components that may have failed.
Through this system, the factory can arrange equipment maintenance plan in advance and shortening the maintenance time and cost. At the same time, through the maintenance schedule can reduce the impact on the factory utilization, maximize customer satisfaction and reach the goal of the smart factory.
Abstract
Regular maintenance of equipment was widely used. The equipment must be shut down for maintenance that according to the time of use or the number of uses after reaching a pre-defined value. As a result, regular downtime maintenance causes capacity Loss and regular replacement of components causes the cost increasing are a major problem. At the same time, the factory must take the risk of unanticipated equipment failure between the maintenance of the equipment and the next maintenance. Such as, emergency repairs result in increased maintenance time, reduced the efficiency of the production line, affected the product yield, and increased the workload of the duty officer.
MIC uses the technology of big data analysis and the machine learning method of artificial intelligence to construct equipment prognostics and health management (PHM) system that ability to provide equipment health assessment and remaining useful life predictions, and about 48 hours early, issue alarm to the components that may have failed.
Through this system, the factory can arrange equipment maintenance plan in advance and shortening the maintenance time and cost. At the same time, through the maintenance schedule can reduce the impact on the factory utilization, maximize customer satisfaction and reach the goal of the smart factory.