Rebooting data-driven soft-sensors in process industries: A review of kernel methods. (May 2020) - Liu, Yiqi; Xie, Min
Highlights: Kernel learning is investigated for data pre-processing, sample selection, variable selection. Online, multi-output, small-data, multi-step and semi-supervised soft-sensors are investigated. Soft-sensors to achieve fault diagnosis and advanced control of process industries are discussed. Potential perspectives on kernel-based soft-sensors are highlighted for future explorations. Abstract: Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process industries, thus allowing for less fault occurrence and better control performance. However, nonlinear, non-stationary, ill-data, auto-correlated and co-correlated behaviors in industrial data always make general data-driven methods inadequate, thus resorting to kernel-based methods provide a necessary alternative. This paper gives a systematic review of various state-of-the-art kernel-based methods with applications for data pre-processing, sample selection, variable selection, model construction and reliability analysis of soft-sensors. An integrated review of various kernel-based soft-sensor modeling methods is attempted, including on-line, multi-output, small-data-driven, multi-step-ahead and semi-supervised applications. The discussion is further to provide an overview of achieving hard-to-measure variable prediction, fault detection and advanced control of process industries. Finally, data-driven soft-sensors with kernel methods perspectives on potential challenges and opportunities have been highlighted for future explorations in the process industrial communities.