Machines · 2015 · 52 citations · 19 references
EngineeringMechanical EngineeringSystems EngineeringAutomationCyber-physical SystemsManufacturing SystemsCyber-physical Systems ArchitectureCyber-physical Systems DesignCyber-physical Systems ModelingIndustrial AutomationIndustrial Cyber-physical SystemsManufacturing AutomationControl Of Cyber-physical SystemsCyber-physical Systems IntegrationHealth State ModelingIndustrial Automation SystemsIndustrial Control SystemsMachine State MonitoringSystem ConfigurationSystem Health Monitoring
Cyber‑physical systems are applied across design, simulation, control, and verification in manufacturing, yet a standard methodology for integrating CPS into data‑analytics workflows is lacking. The study proposes a modeling methodology for a cyber‑physical interface, outlining five information levels from data connection to system configuration, to enable machine‑level health state awareness. It employs adaptive health‑monitoring algorithms and demonstrates the approach on a ball‑screw machine tool, developing a predictive model and interface that links sensor data to system configuration. Results show that machine health state awareness is feasible, and the core technologies can systematically develop CPS, potentially generating additional product revenue and competitive advantage for manufacturers.
Cyber physical systems (CPS) in a manufacturing and automation context can be referred to different manufacturing process, including design, simulation, control, and verification. However, for data analytics, the concept of CPS is relatively new, and a standard methodology is lacking on how to incorporate this type of interface for automation applications. This study discusses a modeling methodology for a cyber physical interface and presents the five levels of information for a cyber physical system, that range from the data connection level to the system configuration level. In order to achieve this awareness and health state of the machine and system, a technical approach that uses adaptive health monitoring algorithms is presented. Lastly, an experimental study on a machine tool ball screw is highlighted, in which a predictive model and a cyber physical interface is developed for this application. The outcomes from this study demonstrate that machine health state awareness is feasible, and the core technologies can aim mechanical systems systematically develop its CPS. This can lead to additional product revenue for the manufacturers, and also a potential competitive edge in the market place.
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