| Résumé | Modeling system behavior plays a vital role in controlling systems and in monitoring systems health. Recently the machine learning-enabled modeling technology has become a powerful technique and tool for developing models for explaining, predicting, and describing system behaviors. In particular, the machine learning-enabled predictive modeling methods have been widely applied to develop the data-driven models from the “big” data in different applications such as system prognostics, system control, and system health management. Over last decade, we have worked on a research program, machine learning-enabled modeling technologies, focusing on the application of machine learning to real-world problems such as control, prognostics, and fault diagnostics. The developed modeling methodologies can help building the machine lerning0vased models form large-sized operational historic data for various domain applications. This paper attempts to summarize the challenge issues facing us and the solutions for addressing these issues. On the same time, the paper also discusses some remaining challenges and future directions. |
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