In this paper, a new sensor network design methodology has been proposed to improve the performance of ICA-based process monitoring approaches. Design procedure incorporates sensor cost, fault detectability and fault detection rate in the design formulation. The design problem has been transformed into an optimization problem. A genetic algorithm (GA) solver has been employed to yield optimal s...

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with Conditional Gaussian Network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example...

In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector A...

The objective of this exercise is to demonstrate how a steady state gain matrix, K can be calculated from a linearized state space dynamic process model. The approach used has been published by Arkun and Downs [1], and it is aimed at handling cases where integrating elements, e.g. liquid levels, are present. The dynamic approach is applied to the Tennessee Eastman process, and the relative gain...

Statistics pattern analysis (SPA) is a new multivariate statistical monitoring framework proposed by the authors recently. It addresses some challenges that cannot be readily addressed by the commonly used multivariate statistical methods such as principal component analysis (PCA) in monitoring batch processes in the semiconductor industry. It was later extended to the monitoring of continuous ...

In this paper, we evaluate multivariate pattern matching methods for the Tennessee Eastman (TE) challenge process. The pattern matching methodology includes principal component analysis based similarity factors and dissimilarity factor of Kano et al., that compare current and historical data. In our similarity factor approach, the start and end times of disturbances are not known a priori and t...

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early maintain safe operations. This study proposed a hybrid model that consists Kernel Principal Component Analysis (kPCA) DNNs can be applied in various processes. complex data is processed by kPCA reduce its dimensionality; then, simplified used for two separate training (detection d...

In order to ensure the safe production of chemical handicrafts, it is necessary monitor process variables system in deal with failures. improve accuracy and monitoring process, whole modeled find out key that cause fault, at same time, impact analyzed propagation path variables. Based on complex network theory, fault node set obtained by combining measured data horizontal visibility map; At con...