Model-driven parametric monitoring of high-dimensional nonlinear functional profiles

In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional profiles that capture rich information on the evolving dynamics of natural and engineered processes. This provides an unprecedented opportunity for online monitoring of operational quality and integrity of complex systems. However, the classical methodology of statistical process control is not concerned about high-dimensional sensor signals and is limited in the capability to perform multi-sensor fault diagnostics. It is not uncommon that multi-dimensional sensing capabilities are not fully utilized for decision making. This paper presents a new model-driven parametric monitoring strategy for the detection of dynamic fault patterns in high-dimensional functional profiles that are nonlinear and nonstationary. First, we developed a sparse basis function model of high-dimensional functional profiles, thereby reducing the large amount of data to a parsimonious set of model parameters (i.e., weight, shifting and scaling factors) while preserving the information. Further, we utilized the lasso-penalized logistic regression model to select a low-dimensional set of sensitive predictors for fault diagnostics. Experimental results on real-world data from patient monitoring showed that the proposed methodology outperforms traditional methods and effectively identify a sparse set of sensitive features from high-dimensional datasets for process monitoring and fault diagnostics.