Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes typically incorporates all measured variables. However, involving variables without beneficial information may degrade performance. This study analyzes the effect of variable selection on principal component analysis (PCA) Then, it proposes a fault-relevant Bayesian inference-based distributed method for efficient fault detection isolation. First, optimal subset is...

Process monitoring is crucial for maintaining favorable operating conditions and has received considerable attention in previous decades. Currently, a plant-wide process generally consists of multiple operational units large number measured variables. The correlation among the variables complex results imperative but challenging such processes. With rapid advancement industrial sensing techniques, data with meaningful information are collected. Data-driven multivariate statistical (DMSPPM)...

The performance of the differential evolution (DE) algorithm is significantly affected by choice mutation strategies and control parameters. Maintaining search capability various parameter combinations throughout entire process also a key issue. A self-adaptive DE with zoning parameters adaptive proposed in this paper. In algorithm, are automatically adjusted population evolution, evolve their own to self-adapt discover near optimal values autonomously. compared five state-of-the-art...

Plasmonic MoO3-x@MoO3 nanosheets obtained from surface oxidation of MoO3-x were employed as a SERS substrate for methylene blue detection. They exhibit extraordinary sensitivity comparable to noble metals, which is attributed shell-isolated electromagnetic enhancing by the plasmonic core and elimination photocatalytic degradation MoO3 shell.

Synthetic minority oversampling technique (SMOTE) has been widely used in dealing with the imbalance classification problem machine learning field. However, classical SMOTE implements by linear interpolation between adjacent class samples, which may fail to consider uneven distribution of samples. This article proposes a clustering (MC-SMOTE) method that involves samples improve performance. First, from are clustered into several clusters. Second, is performed clusters create new different...

Batch-end quality modeling is used to predict the by using batch measurements and generally involves a large number of predictor variables. However, not all variables are beneficial for prediction. Conventional multiway partial least squares (PLS) may function properly batch-end because many irrelevant This paper proposes an optimized sparse PLS (OSPLS) approach simultaneous prediction relevant-variable selection. The effect on quality-prediction performance analyzed, importance selection...

Large-scale processes have become common, and fault detection for such is imperative. This work studies the data-driven distributed local problem large-scale with interconnected subsystems develops a genetic algorithm (GA)-regularized canonical correlation analysis (CCA)-based scheme. For each subsystem, GA-regularized CCA first performed its all coupled systems, which aims to preserve maximum minimal communication cost. A CCA-based residual then generated, corresponding statistic...

Sensitive principal component analysis (SPCA) is proposed to improve the (PCA) based chemical process monitoring performance, by solving information loss problem and reducing nondetection rates of T2 statistic. Generally, components (PCs) selection in PCA-based subjective, which can lead poor performance. The SPCA method subsequently build a conventional PCA model on normal samples, index PCs reflect dominant variation abnormal observations, use these sensitive (SPCs) monitor process....

Industrial big data and complex process nonlinearity have introduced new challenges in plant-wide monitoring. This article proposes a local-global modeling distributed computing framework to achieve efficient fault detection isolation for nonlinear processes. First, stacked autoencoder is used extract dominant representations of each local unit establish the inner monitor. Second, mutual information (MI) determine neighborhood variables unit. Afterward, joint representation learning then...

Process monitoring based on deep learning has attracted considerable attention. Generally, several hidden layers exist in the deep-learning model, and only output information of last layer neurons extracted by is applied. Considering that each a kind representation original data, different may contain positive elements for process monitoring. In this article, we found when fault occurs, there are some they different, compared with normal condition. These called unstable neurons. Obviously,...

Although principal component analysis (PCA) is widely used for chemical process monitoring, improvements in the selection of components (PCs) are still needed. Given that determination complicated and changing fault information not guaranteed using offline‐selected PCs, this study proposes a just‐in‐time reorganized PCA model objectively selects PCs online monitoring. The importance evaluated by kernel density estimation. indicating more varied then selected to reorganize model. most useful...

Traditional process monitoring methods take all the measured variables into account, whereas it will be inappropriate for indicating quality-relevant faults. Some are independent from quality and these redundancy no doubt degrade prediction performance of variables. This paper proposes a novel relevant two block scheme based on mutual information (MI) kernel principal component analysis (KPCA). First, divided subblocks according to their MI value with Then, KPCA monitors subblock...

Decentralized process monitoring based on purely data-based methods has recently gained considerable attention in multivariate statistical circle. Although the variables can be divided into several blocks automatically according to their preferences, most of existing multiblock modeling strategies tends build local models individually, where relevance among different is ignored, and this leaves a room for enhancing performance. Inspired by recognition lack, modified principal component...

Industrial processes generally have various operation modes, and fault detection for such is important. This paper proposes a method that integrates variational Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA) efficient multimode process monitoring. The proposed VBGMM-CCA maximizes the advantage of VBGMM in automatic mode identification superiority CCA local detection. First, applied to unlabeled historical data determine number modes cluster each mode. Second,...