Deep learning has recently emerged as a promising method for nonlinear process
monitoring. However, ensuring that the features from process variables have representative …

This article proposes an integrated approach of model-based and data-driven gap metric
fault detection and isolation in a stochastic framework. For actuator and sensor faults, an …

Chemical process modeling is the basis for research and applications in related fields. With
the development of industrial informatization, data-driven process modeling techniques are …

This article develops a low-cost dual-input multisensor odor detection system relying on
sensor detection technology and the changing characteristics of the odor itself. The finite …

A novel statistical model based on the higher-order statistics projection to the latent structure
(HPLS) is proposed, which uses a combination of higher-order statistics (mutual information …

It is crucial to adopt an efficient process monitoring technique that ensures process
operation safety and improves product quality. Toward this endeavor, a modified canonical …

In this article, an optimal data-driven approach is proposed to deal with the problem of
distribution independent fault detection (FD) for stochastic linear discrete-time systems. For …

This paper presents a multivariate linear contribution analysis in the context of fault
detection, isolation and diagnosis. The usually univariate contribution analysis in fault …

Decarbonization of the oil refining industry is essential for reducing carbon emissions and
mitigating climate change. Co-processing bio feed at existing oil refineries is a promising …

In order to satisfy the safety requirements of plant-wide processes, distributed process
monitoring methods are often used. However, few of them consider the problem on building …