System Control and Diagnosis in Data-Rich Environment

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Owing to the fast development of computer and network technology, nowadays processes and control systems are enhanced by rich measurement data from numerous sensors of multiple sources and locations. In large-scale manufacturing and chemical processes, application of distributed control systems (DCS) has created such a data-rich environment. Real-time measurements as well as historical data from different locations and sources are available for processing. Meanwhile, due to the lack of effective tools digging in the massive amount of data for relevant and useful information, industries are facing the so-called “data rich but information poor” syndrome.****There are several situations where data becomes essential and critical in decision-making. First of all, systems and processes are mostly operated under well-controlled constraints that are well understood in the initial design stage. For many systems and processes, first-principle models and process knowledge are available and can be used for control and performance analysis/evaluation under the normal operation conditions. However, when unexpected conditions emerge, e.g. fault related or other time-varying operations, system operations deviate from its initially designed conditions, and fidelity of those models suffers. In this case, data becomes important for system performance evaluation, fault diagnosis, updating of the control law and even condition monitoring based maintenance. How to extract useful up-to-date information from multiple sources of data and fuse' it with knowledge from the physical model has raised attention from industries and research communities. Furthermore, in many practical systems, traditional time-series measurement, as well as measurement of alternate forms, e.g. distribution functions, and spectroscopic data, are available. They all carry vital information about the system. How to extract useful information from all available data of different form for control and system fault diagnosis is a new challenge. Another situation where data is essential arises when the system and process is highly complex and the physical model is not even valid. In this case, data-driven modeling and important process analytics become almost the only means.****In this research program, we will focus on some of the new challenges facing conventional system control and fault diagnosis methodologies in the new data-rich paradigm. The long-term objective is aimed at developing effective design and analysis tools for control and diagnosis of systems/processes that are capable of processing data of different forms and/or from multiple sources (timely and spatially). These tools are essential for the development of the new generation information system and control/instrumentation devices for the new industry standard, such as Industrial Internet of Things (IIoT). *** **