Abstract The previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well monitoring processes when used for fault diagnosis. In order to validate effectiveness data-driven analysis methods in field diagnosis, it is necessary conduct corresponding experiments. Therefore this chapter introduces two kinds simulation platform, Tennessee E...

This article employs adaptive rank-order morphological filter to develop a pattern classification algorithm for fault diagnosis in benchmark chemical process: Tennessee Eastman process. Rank-order filtering possesses desirable properties of dealing with nonlinearities and preserving details in complex processes. Based on these benefits, the proposed algorithm achieves pattern matching through a...

Although many PID tuning approaches are available, it is not easy to find a method that does not require any engineer/operator interference. In this work, we present a fully automated approach for PID tuning based on relay feedback. This method involves sending the relay feedback test data from PLCs (Programmable Logic Controller) into a historian, analyzing the test data using a tuning applica...

In this article, we use a genetic algorithm to obtain an economically optimal preventive maintenance frequency for different equipment, the parts inventory policy (number and type of spare parts to keep in stock), and labor allocation in process plants. To assess cost, we improved a previously published Monte Carlo simulationbased maintenance model (Nguyen et al. Ind. Eng. Chem. Res. 2008, 47(6...

In this paper, a cumulative sum based statistical method is used to detect faults in the Tennessee Eastman Process (TEP). The methodology is focused on three particular faults that could not be observed with other fault detection methodologies previously reported. Hotelling’s-T charting based on the cumulative sums of the faults’ relevant variables was successful in detecting these faults, howe...

This paper presents a fault diagnosis procedure based on discriminant analysis and mutual information. In order to obtain good classification performances, a selection of important features is done with a new developed algorithm based on the mutual information between variables. The application of the new fault diagnosis procedure on a benchmark problem, the Tennessee Eastman Process, shows bet...

This paper proposes a combined canonical variate analysis (CVA) and Fisher discriminant analysis (FDA) scheme (denoted as CVA–FDA) for fault diagnosis, which employs CVA for pretreating the data and subsequently utilizes FDA for fault classification. In addition to the improved handling of serial correlations in the data, the utilization of CVA in the first step provides similar or reduced dime...