For a non-Gaussian process, a kernel principal component analysis that is applied to handle a Gaussian process is used to calculate a whitening matrix using the conventional kernel independent ...component analysis (KICA). Some errors exist as the orthogonal matrix is calculated by negentropy, which is an approximate method. In this paper, a kernel-independence-criterion-based independent component analysis algorithm for fault monitoring is proposed. The main contributions are as follows: 1) kernel independence criterion in regeneration Hilbert space is given. Based on which, an exact objective function is given. Compared with the conventional KICA, the accuracy of calculation is enhanced, and the proposed method is applied to any twice differentiable kernel function. 2) High computational efficiency is achieved by the quasi-Newton method that has a rapid convergence on the objective function. 3) The proposed method provides more stability to the local minimum value when the initialization data are far away from independent. The performance of the proposed method is illustrated by a numerical example and the penicillin fermentation process. Compared to the conventional KICA method, the experimental results show the advantages and effectiveness of the proposed approach.
In this paper, a new modeling approach is proposed for common and specific feature extraction. The original space of a mode can be separated into two different parts, namely, the common and specific ...ones. There are both non-Gaussian similarity and dissimilarity in the underlying correlations of different modes. After two different non-Gaussian blocks are separated, one can obtain the common and specific blocks, respectively. They play different roles in industrial batch processes, which are referred to as repetitive and complementary effects, respectively. Then, the common block and specific block are analyzed. A new multiblock monitoring method is proposed and the monitoring process is carried out in each block. The proposed method is applied to process monitoring of a continuous annealing process. Application results indicate that the proposed approach effectively captures the non-Gaussian relations to build the process model and improves the detection ability.
► A multiblock kernel independent component analysis (MBKICA) algorithm is proposed. ► A new fault diagnosis approach based on MBKICA is proposed to monitor large-scale processes. ► The nonlinearity ...and non-Gaussianity in the block process variables are extracted.
In this paper, a multiblock kernel independent component analysis (MBKICA) algorithm is proposed. Then a new fault diagnosis approach based on MBKICA is proposed to monitor large-scale processes. MBKICA has superior fault diagnosis ability since variables are grouped and the non-Gaussianity is considered compared to standard kernel methods. The proposed method is applied to fault detection and diagnosis in the continuous annealing process. The proposed decentralized nonlinear approach effectively captures the nonlinear relationship and non-Gaussianity in the block process variables, and shows superior fault diagnosis ability compared to other methods.
In this brief, a new fault isolation method is proposed. The disadvantages of the conventional contribution plot method are: 1) the fault cannot be identified accurately due to process control and ...recycle loops in process flowsheets. To overcome this disadvantage, the fault-relevant-independent components (ICs) are extracted in this brief, which clearly represent different fault feature and 2) the conventional fault identification method is not available for the nonlinear process. In this brief, the nonlinear fault direction information is extracted. Then, the fault isolation method in the nonlinear process is proposed, where the historical fault information is used to build the model. The proposed method is applied to a simple nonlinear process and the electro-fused magnesia process. Compared with IC analysis (ICA) method and kernel ICA method, the results clearly show the effectiveness of the proposed method.
The present communication demonstrates a relatively green preparative route toward Au nanoplates in aqueous solution at room temperature with the use of tannic acid (TA), which is an environmentally ...friendly, soluble polyphenol, as a reducing agent. Such Au nanoplates exhibit notable catalytic performance toward the oxidation and reduction of H2O2. A glucose biosensor was further fabricated by immobilizing glucose oxidase (GOD) into chitosan–Au nanoplate composites film on the surface of glassy carbon electrode (GCE). This sensor exhibits good response to glucose, and the linear response range is estimated to be from 2 to 20mM (R=0.999) at 0.65V and from 2 to 10mM (R=0.993) at −0.2V, respectively. The sensitivity of the sensor determined from the slopes is 49.5μAmM−1cm−2 at 0.65V.
In this paper, first, some disadvantages of original partial least squares method of independent component analysis (ICA-PLS) are analyzed. Then ICA-PLS is modified for regression purpose.
...Disadvantages of the original ICA-PLS algorithm are as follows: 1) the regression coefficient matrix and residual matrix cannot been given so that the computation time may increase with the number of samples; and 2) ICA-PLS lacks the ability to give better monitoring performance when the correlation structure of measured variables is nonlinear, which is often the case for industrial processes.
To solve the above problems, we modified the original algorithm in following aspects: 1) the regression coefficient matrix and residual matrix in ICA-PLS are given so that the computation time is decreased; and 2) to solve the nonlinear problem, ICA-PLS and kernel trick is first combined for nonlinear regression purpose, which is called iterative ICA-KPLS in this paper. The iterative calculation of ICA-KPLS will be time consuming when the sample number becomes larger. Hence, the regression coefficient matrix and residual matrix in ICA-KPLS are given to avoid the expensive computation time when the number of samples is huge.
The proposed methods are applied to the quality prediction in fermentation process and Tennessee Eastman process. Applications indicate that the proposed approach effectively captures the relations in the process variables and use of ICA-KPLS instead of ICA-PLS improves the predictive ability. The expensive computation time is avoided by using the coefficient matrix and residual matrix.
Macroscopic supramolecular assembly (MSA) is a newly established methodology to construct supramolecular materials directly from large building blocks. Demonstrations of MSA for various functions are ...urgently needed to advance MSA from fundamental studies to practical uses. Here we propose the fabrication of DNA microarrays by combining MSA and magnetic-assisted localization.
In this paper, a novel approach to fault detection for nonlinear processes is proposed. It is based on a manifold learning called modified kernel semi-supervised local linear embedding. Local linear ...embedding (LLE) is widely applied to fault detection of complex industrial process. However, the LLE only preserves the local structure information of the sample, which ignores the global characteristics of the original data. The main contributions of the presented approach are as follows: 1) in order to utilize labeled data, the semi-supervised learning is introduced into LLE; 2) the regularization term is added to the calculation of local reconstruction weights matrix to strengthen the anti-noise ability in nonlinear processing; and 3) in order to extract the global and local characteristic of the observation data, the kernel principal component analysis objective function is integrated with the objective function of LLE. Experimental results on the production process of fused magnesia verify the performance of the proposed method.
► Hierarchical kernel partial least squares is proposed for monitoring batch processes. ► HKPLS gives more nonlinear information compared to HPLS and MPLS. ► HKPLS does not need to estimate or fill ...in the unknown part of the process. ► HKPLS show superior fault detectability in process monitoring.
In this paper, new monitoring approach, hierarchical kernel partial least squares (HKPLS), is proposed for the batch processes. The advantages of HKPLS are: (1) HKPLS gives more nonlinear information compared to hierarchical partial least squares (HPLS) and multi-way PLS (MPLS) and (2) a new batch process monitoring using HKPLS does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The proposed method is applied to the penicillin process and continuous annealing process and is compared with MPLS and HPLS monitoring results. Applications of the proposed approach indicate that HKPLS effectively capture the nonlinearities in the process variables and show superior fault detectability.
We report the successful construction of plasmonic core–satellite nanostructured assemblies on two-dimensional substrates, based on a strategy of combining DNA-functionalized plasmonic nanoparticles ...(NPs) with the specific recognition ability toward target to enable satellite NPs to self-assemble around the core immobilized on substrates. A strongly coupled plasmonic resonance band was observed because of the close proximity between core and satellite NPs, which presented significant red-shift and enhanced extinction with respect to the local surface plasmon resonance (LSPR) band of individual core NPs on the substrate. The functionality of this core–satellite nanostructured assembly as a biosensor was further explored, and the changes in extinction intensity and the peak shift of the plasmonic coupling resonance band arising from the probe-target DNA binding event all proved to be useful criteria for target DNA detection. Moreover, high selectivity down to single-base mismatched DNA was achieved using this strongly coupled plasmonic core–satellite nanostructured assembly on a substrate. Such substrate-based detection was advantageous, and its reusability and high cycle stability were demonstrated after five cycles of disassembly and reassembly. Our work demonstrates the biosensing capacity of this DNA-functionalized plasmonic nanoassembly model system on two-dimensional substrate, which is also applicable to the detection of numerous DNA-recognized biomolecules. Likewise, the presented construction method can be extended to fabricate other compositional core–satellite nanoassemblies.
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