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...

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...

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...

This paper presents a new online statistical monitoring based on dynamic independent component analysis (DICA) to detect the Tennessee Eastman challenge process faults. The proposed method employs dynamic feature extraction approach to capture most of the inherent dynamic fault information. This leads to an efficient fault detection with superior performance compared to independent component an...

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...

When a fault occurs in process, it slowly propagates within the system and affects measurements triggering sequence of alarms control room. The operators are required to diagnose cause take necessary corrective measures. idea representing alarm as propagation path using is explored. A diagnoser based on hidden Markov model built identify signals. proposed approach applied an industrial case stu...

Industrial processes are large scale, highly complex systems. The flow of mass and energy, as well the compensation effects closed-loop control systems, cause significance cross-correlation autocorrelation between process variables. To operate systems stably efficiently, it is crucial to uncover inherent characteristics both variance structure dynamic relationship. Long-term dependency slow fea...