Abstract The crossover among two or more types of diffusive processes represents a vibrant theme in nonequilibrium statistical physics. In this work we propose models to generate crossovers different Levy processes: the first model change gradually order derivative Laplacian term diffusion equation, whereas second one consider combination fractional-derivative terms characterized by coefficient...

On-line monitoring of process variability is strategic to achieve high standards of quality and maintain at acceptable levels the number of nonconforming items. Shewhart control charts are the simplest Statistical Process Control (SPC) procedure to achieve this goal. An efficient implementation of a control chart requires the optimal selection of its design parameters. They can be selected acco...

Abstract : The monitoring of industrial chemical plants and diagnosing the abnormalities in those set ups are crucial in process system domain as they are the deciding factors for the betterment of overall production quality in the process. Various statistical based malfunction detection methods have been included in the literature, namely, univariate and multivariate techniques. The univariate...

BACKGROUND The Women's Health Initiative (WHI) randomized trial of estrogen plus progestin (E + P) was terminated early based on an assessment of harms exceeding benefits for disease prevention. The results contravened prevailing wisdom and a large body of literature regarding benefits of menopausal hormone therapy. The results and their interpretation have been the subject of considerable deba...

Probabilistic models, including probabilistic principal component analysis (PPCA) and PPCA mixture models, have been successfully applied to statistical process monitoring. This paper reviews these two models and discusses some implementation issues that provide alternative perspective on their application to process monitoring. Then a probabilistic contribution analysis method, based on the co...

This paper proposes a new method to detect abnormal process state. The method is based on cluster center point monitoring in time and is demonstrated in its application to data from Olkiluoto nuclear power plant. Typical statistical features are extracted, mapped to ndimensional space, and clustered online for every time step. The process signals in the constant time window are classified into ...

Fault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. In this paper a comparison of four monitoring methods, PCA, PLS, subspace identification and self-organising maps, for fault detection of the online analysers in a dearomatisation process is presented. The effectiveness of different s...

The paper presents the application of several process control-related methods to the monitoring and control of data quality in financial databases. The quality control process itself can be seen as a classical control loop. Measurement of the data quality is conducted via application of quality tests, which exploit data redundancy defined by meta-information or extracted from data by statistica...

A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Comparedwith traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-rel...

In this paper, an approach of wavelet-based nonlinear PCA for statistical process monitoring is presented. The strategy utilizes the optimal wavelet decomposition in such a way that only approximation and the highest detail functions are used thus simplifying the overall structure and making the interpretation at each scale more meaningful. An orthogonal nonlinear PCA procedure is incorporated ...