Advanced Methods in Diagnostics and Prognostics

This article provides a tutorial on data-driven methods for diagnostics and prognostics. The methods span the range from classical methods to machine learning-based methods including ensemble methods and support vector machine. The main challenges associated with diagnostics/prognostics are discussed, and decision rules are provided for selecting which data-driven modeling method to apply to a particular dataset based on the characteristics of the data. The autonomous selection of data-driven methods is described, as an application-specific automated machine learning (AutoML) approach. A case study of a simulated chemical plant provided by Tennessee Eastman Company is used to illustrate the characteristics of the data-driven methods for diagnostics and prognostics.