For multimode processes, it is inevitable to encounter disturbances, such as equipment aging, catalyst deactivation, sensor drifting, reaction kinetics or adding new operating modes. The existing monitoring algorithms are established either for coping with feature under time-invariant circumstance handling the time-varying problem of processes single mode. purpose this article develop an effective modeling and approach complex both properties. We propose a novel adaptive scheme based on...

Traditional monitoring algorithms use the normal data for modeling, which are universal different types of faults. However, these may perform poorly sometimes because lack fault information. In order to further increase detection rate while preserving universality algorithm, a novel dynamic weight principal component analysis (DWPCA) algorithm and hierarchical strategy proposed. first layer, PCA is used diagnosis, if no detected, following DWPCA-based second layer will be triggered....

Complex chemical processes often have multiple operating modes to meet changes in production conditions. At the same time, within-mode process data usually follow a complex combination of Gaussian and non-Gaussian distributions. The multimodality distribution uncertainty multimode make conventional multivariate statistical monitoring (MSPM) methods unsuitable for practical processes. In this work, novel method called neighborhood standardized local outlier factor (NSLOF) is proposed. each...

Process monitoring is an effective means to ensure process safety and improve product quality. On the one hand, it possible that fault will not affect or other every affects both quality simultaneously. To make more purposeful accurate, a novel performance-indicator-oriented concurrent subspace (PIOCS) method containing three subspaces with different degrees of importance proposed in this paper. The first safety-related subspace, second safety-unrelated quality-related third unrelated...

In this paper, we extend the stability theory on Kalman filtering with intermittent measurements from scenario of one single sensor to multiple sensors. Consider that a group sensors take measurement states process and then send data remote estimator. The estimator receives intermittently, which may be caused by fact channels have packet dropouts or schedule transmission stochastically. Based received measurements, computes estimates multi-sensor filtering. Because unstable. This issue is...

Plant-wide processes often have the characteristics of large-scale and multiple operating units. Moreover, due to closed-loop control, it is possible that fault never affects product quality. In this article, a novel data-driven method called multisubspace orthogonal canonical correlation analysis (CCA) proposed, which can not only tell whether occurs but also judge quality in real time. First, reduce process complexity construct an accurate monitoring model, original variable space divided...

Industrial processes are developing towards intelligence and complexity, which brings challenges to intelligent process monitoring. An effective fault diagnosis model plays a vital role in ensuring safety. However, labeling samples is time-consuming costly, make it hard obtain enough labeled train an diagnostic model. This motivates the development of semi-supervised learning basic idea use unlabeled data help limited for training. In this paper, consistency regularization auto-encoder...

As industrial technology develops, processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent condition of whole process effect fault on quality indicators. Therefore, a novel multiblock decouple convolutional neural network (multiblock DCN) algorithm is proposed. First, key variables selected, grouped into multiple blocks for following monitoring. Then, in each block, proposed DCN constructs regression model between...

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. processes, the sampling rates of quality variables are always lower than those process variables. Meanwhile, among also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve problem, a deep-learning (DL) model based on multitemporal channels convolutional neural network (MC-CNN) is proposed....

To solve the problem of incipient fault detection, a targeted gated recurrent unit-canonical correlation analysis (CCA) method is proposed. First, this article proposed unit (FTGRU) to establish temporal feature extraction model. The features extracted by FTGRU are more sensitive faults, thus increasing accuracy detection Then, model established CCA method. In addition, in order ensure universality model, multilayer strategy At first layer, basic used. When no detected at second layer...

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Abstract Recent research has shown that metabolic processes within immune cells are essential for both human immunodeficiency virus 1 (HIV-1) infection and the response. Throughout HIV-1 infection—from acute stages to chronic viral latency—immune experience shifts in energy demands pathways, paralleling T-cell exhaustion. Dysregulated metabolism compromises cell function, leading dysfunction persistent inflammation. Therefore, alterations constitute a critical mechanism progression This...