Bayesian methods are a kind of data-driven methods developed in recent years and have played an important role in fault detection and diagnosis. Nevertheless, traditional Bayesian fault detection methods cannot deal with the case where some underlying modes are ambiguous in the historical data. To cope with this problem, a new method is presented in this paper. The modes with uncertainty in his...

Complex process plants increasingly appear in modern chemical industry due to the considerable economic efficiency that complex and interactive process designs can offer. Interactions between process units (e.g., material recycle and energy integration) often cause significant difficulties in plantwide control. As such, it is important to study plantwide operability (i.e., whether a plantwide p...

The actual multimodal process data usually exhibit non-linear time correlation and non-Gaussian distribution accompanied by new modes. Existing fault diagnosis methods have difficulty adapting to the complex nature of modalities are unable train models based on small samples. Therefore, this paper proposes a modal method meta-learning (ML) neural architecture search (NAS), MetaNAS. Specifically...

Adaptation of network weights using LevenbergMarquardt (LM) training algorithm was proposed as a mechanism to improve the performance of Artificial Neural Networks (ANN) in modeling the Tennessee Eastman (TE) chemical process reactor. A Neural Network of the AutoRegressive, eXternal (NNARX) input model was developed. Four sub-models for the TE reactor were built. They are: the reactor level, th...

The Tennessee Eastman (TE) Control Challenge proposed by Downs and Vogel [1] is a test bed problem for use in evaluating advanced process control methodologies from a plant-wide perspective. The dynamic model for the process (based on an actual industrial process) integrates the operation of five unit operations; viz. an exothermic, two-phase reactor, a partial condenser, a centrifugal compress...

The Tennessee Eastman Challenge Process (TECP) represents an interesting case study within PSE for process control and process optimization purposes. It has been widely addressed by the chemical engineering research community since its publication. The TECP is an open-loop unstable recycle reactor. The eigenvalues of the Jacobean matrix of the system at the base-case steady-state conditions, ra...

In this article we propose a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis. The SDG is the rst level in our framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA, which uses the temporal evolution of the sensors for f...

-This paper proposes a new method for online identification of a nonlinear system using RKHS models. The RKHS model is a linear combination of kernel functions applied to the used training set observations. For large datasets, this kernel based to severs computational problems and makes identification techniques unsuitable to the online case. For instance, in the KPCA scheme the Gram matrix ord...