Abstract
Complexity and performance requirements of the control systems are increasing dramatically along with fault diagnosis and predictive maintenance as transformation of industry 4.0 continues. Hence, both literature and industry requires a comprehensive and effective predictive maintenance and health monitoring tools. There are many different wellestablished classical approaches for predictive maintenance but a systematic inclusion of smartness to this context is still missing in the field. In this study, we propose a Smart Predictive Maintenance framework enabled by key Industrial Artificial Intelligence technologies of Digital Twins and Smart Big Data. The framework includes steps of Digital Twin development along with the utilization of Smart Big Data in the sense of Predictive Maintenance along with the application of the frontier to an important problem of RF Impedance Matching.
Published Version
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