Conventional Principal Component Analysis Research Articles

Data-driven method for monitoring and diagnosis in energy system of papermaking process

This study proposes a data-driven model for monitoring and diagnosis in energy system of papermaking process based on kernel component analysis (KPCA) and the kernel slow feature analysis (KSFA). Four different abnormal patterns are designed, while the false alarm rate (FAR) and the missed detection rate (MDR) are used to evaluate the validity of the proposed model. The performance of KPCA is better than that of conventional PCA and KSFA. The online monitoring and diagnosis analysis of energy utilization are achieved based on the historical data and the proposed model. The production status and the energy consumption level across the whole process can be acquired. The results demonstrate that the proposed method is effective. It can provide the valuable reference foundation for further energy analysis and optimization in papermaking process.

An improved registration strategy for aligning incomplete blade measurement data to its model

Among the existing global registration methods, the principal components analysis (PCA) combined with iterative closest point (ICP) algorithm is probably the easiest and efficient way to implement global alignment. However, this method has limitations in processing the registration of two-point clouds with large difference in shape. To aiming this problem, an improved MRPCA-ICP method is proposed to align the incomplete blade measurement data to its CAD model. In this method, we firstly define and extract the major region (MR) of blade using the constraint plane constructed on the tenon of blade. Then the MRPCA method is presented to replace the conventional PCA to achieve coarse alignment, and a simple and efficient algorithm is particularly developed to ensure the consistent orientation of corresponding principal directions. Finally, the ICP-based fine registration is accelerated for the better initial input provided by MRPCA. Experimental results demonstrate the superiority of our method in terms of the registration efficiency and robustness.

Multivariate nuisance alarm management in chemical processes

Alarm systems are of vital importance in the safe and effective functioning of industrial plants, yet they frequently suffer from too many nuisance alarms (alarm overloading). It is necessary to intelligently enhance existing alarm systems and supply accurate information for the operators. Nowadays, process variables are more correlated and complicated. This correlation structure can be used as a basis to manage alarms efficiently. Hence, multivariate approaches are more appropriate. Designing a system aimed at reducing nuisance alarms is an essential phase to guarantee the reliable operation of a plant. Due to the definition of alarm limits, the problem of false alarms is inevitable in multivariate methods. In this paper, the conventional Principal Component Analysis (PCA) is applied to extract the sum of squared prediction error (SPE) known as the Q statistic and the Hotelling T2 statistic. These statistics are used separately as alarm indicators where their control limits are duly modified. Consequently, for each statistic, a nonlinear combination of alarm duration and alarm deviation, is additionally exploited as a new requirement to activate an alarm or not. The resulting new index is fed to a delay timer with a defined parameter n. The implementation of this technique resulted in a significant reduction in the severity of alarm overloading. Historical data collected from the cement rotary kiln operating under healthy conditions are employed to adequately build the PCA model and extract the proposed alarming indexes. Then, various testing data sets, covering different types of faults occurring in the cement process, are used to assess the performance of the developed method. In comparison with the conventional PCA technique, alarms are better managed nd almost nuisance alarms are suppressed. The proposed method is more robust to false alarms and more sensitive to fault detection.

Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula

This paper presents a risk-based fault detection and diagnosis methodology for nonlinear and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine model provides a multivariate probability that is used in the event tree to generate a dynamic risk profile. An abnormal situation is detected from the monitored risk profile; subsequently, root cause(s) diagnosis is carried out. A fault diagnosis module is also proposed using the density quantiles, developed from marginal probabilities. The performance of this methodology is benchmarked using the Tennessee Eastman chemical process. The proposed risk-based framework has also been applied to an experimental setup and a real industrial isomer separator unit. The diagnosis module is found sensitive to both single and simultaneous faults. The results confirm that the proposed methodology provides better performance than the conventional principal component analysis and transfer entropy-based fault diagnosis techniques using the advantage of marginal density quantile analysis.

Aberration-free digital holographic phase imaging using the derivative-based principal component analysis.

.Significance: Digital holographic microscopy is widely used to get the quantitative phase information of transparent cells.Aim: However, the sample phase is superimposed with aberrations. To quantify the phase information, aberrations need to be fully compensated.Approach: We propose a technique to obtain aberration-free phase imaging, using the derivative-based principal component analysis (dPCA).Results: With dPCA, almost all aberrations can be extracted and compensated without requirements on background segmentation, making it efficient and convenient.Conclusions: It solves the problem that the conventional principal component analysis (PCA) algorithm cannot compensate the common but intricate higher order cross-term aberrations, such as astigmatism and coma. Moreover, the dPCA strategy proposed here is not only suitable for aberration compensation but also applicable for other cases where there exist cross-terms that cannot be analyzed with the PCA algorithm.

Efficient tools for principal component analysis of complex data— a tutorial

Basic tools for exploration and interpretation of Principal Component Analysis (PCA) results are well-known and thoroughly described in many comprehensive tutorials. However, in the recent decade, several new tools have been developed. Some of them were originally created for solving authentication and classification tasks. In this paper we demonstrate that they can also be useful for the exploratory data analysis.We discuss several important aspects of the PCA exploration of high dimensional datasets, such as estimation of a proper complexity of PCA model, dependence on the data structure, presence of outliers, etc. We introduce new tools for the assessment of the PCA model complexity such as the plots of the degrees of freedom developed for the orthogonal and score distances, as well as the Extreme and Distance plots, which present a new look at the features of the training and test (new) data. These tools are simple and fast in computation. In some cases, they are more efficient than the conventional PCA tools. A simulated example provides an intuitive illustration of their application. Three real-world examples originated from various fields are employed to demonstrate capabilities of the new tools and ways they can be used. The first example considers the reproducibility of a handheld spectrometer using a dataset that is presented for the first time. The other two datasets, which describe the authentication of olives in brine and classification of wines by their geographical origin, are already known and are often used for the illustrative purposes.The paper is written in the form of tutorial; however, we do not touch upon the well-known things, such as the algorithms for the PCA decomposition, or interpretation of scores and loadings. Instead, we pay attention primarily to more advanced topics, such as exploration of data homogeneity, understanding and evaluation of an optimal model complexity. The tutorial is accompanied by links to free software that implements the tools.

Sensible Functional Linear Discriminant Analysis Effectively Discriminates Enhanced Raman Spectra of Mycobacterium Species.

Tuberculosis caused by Mycobacterium tuberculosis complex (MTBC) is one of the major infectious diseases in the world. Identification of MTBC and differential diagnosis of nontuberculous mycobacteria (NTM) species impose challenges because of their taxonomic similarity. This study describes a differential diagnosis method using the surface-enhanced Raman scattering (SERS) measurement of molecules released by Mycobacterium species. Conventional principal component analysis and linear discriminant analysis methods successfully separated the acquired spectrum of MTBC from those of NTM species but failed to distinguish between the spectra of different NTM species. A novel sensible functional linear discriminant analysis (SLDA), projecting the averaged spectrum of a bacterial specie to the subspace orthogonal to the within-species random variation, thereby eliminating its influence in applying linear discriminant analysis, was employed to effectively discriminate not only MTBC but also species of NTM. The successful demonstration of this SERS-SLDA method opens up new opportunities for the rapid differentiation of Mycobacterium species.

Some Multilinear Variants of Principal Component Analysis: Examples in Grayscale Image Recognition and Reconstruction

Principal component analysis (PCA) has long been used in computer vision applications such as face recognition. Here, we present an overview of some variants of PCA, including 2D PCA (2DPCA), bidirectional 2DPCA (B2DPCA), and coupled subspace analysis (CSA). Unlike conventional PCA, the variants 2DPCA, B2DPCA, and CSA preserve the original image structure, often providing better recognition and reconstruction results than those obtained with PCA. This article considers the background for these techniques and steps involved in applying these methods, including typical preprocessing of sample images, algorithm description, and classification. These variants of PCA have been successfully used in a number of different areas such as identification of wood species, biometrics (not limited to face recognition), medical imaging, and image compression, to name a few examples; we briefly mention some of these to provide an idea of the scope of applications. We address some advantages and disadvantages of these variants in relation to PCA. Utilizing the Modified National Institute of Standards and Technology (MNIST) digits and Fashion-MNIST image sets, we demonstrate application of CSA for image recognition and reconstruction compared to PCA. Finally, we mention how these PCA variants fit into a more general framework using tensors.

Terahertz photoconductive antenna with embedded electrodes: simulation and experiment

The paper presents the results of numerical simulation of a terahertz (THz) photoconductive antenna with embedded electrodes by the finite element method. The simulation results indicate the fact that the proposed THz antenna has a higher photocurrent than the conventional photoconductive antenna with conventional electrode contacts. Higher THz power can potentially be obtained using the proposed photoconductive antenna with embedded electrodes. The simulation results show that the electric field strength at the surface is higher for conventional PCA, however, the PCA depth with embedded contacts has a higher electric field strength. The simulation results show that the increase in the photocurrent is directly proportional to the thickness of the embedded contacts. The results of the performed experiments are consistent with the conclusions of the simulation.

Local component based principal component analysis model for multimode process monitoring

In order to achieve improved the performance of conventional principal component analysis in multimode processes, we have investigated the local component to eliminate the impact of multimode properties. • LCPCA can eliminate the impact of multimode features on the conventional PCA method. • LCPCA can automatically identify the number of the process operating conditions. • LCPCA can provide suitable data foundation for monitoring process. For plant-wide processes with multiple operating conditions, the multimode feature imposes some challenges to conventional monitoring techniques. Hence, to solve this problem, this paper provides a novel local component based principal component analysis (LCPCA) approach for monitoring the status of a multimode process. In LCPCA, the process prior knowledge of mode division is not required and it purely based on the process data. Firstly, LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture (FGMM). Then, calculating the posterior probability is applied to determine each sample belonging to which local component. After that, the local component information (such as mean and standard deviation) is used to standardize each sample of local component. Finally, the standardized samples of each local component are combined to train PCA monitoring model. Based on the PCA monitoring model, two monitoring statistics T 2 and SPE are used for monitoring multimode processes. Through a numerical example and the Tennessee Eastman (TE) process, the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.

Fault diagnosis Based on An Improved Statistics Principal Analysis Similarity Factor

To settle the issue of conventional principal component analysis similarity factor cannot take advantage of higher-order statistics information of process variables, we presented an improved statistics principal analysis similarity factor (SPASF) to identify fault patterns in our work. In the improved SPASF approach, process data is first converted from original space into a new statistics space by the means of statistics pattern analysis (SPA) technology, and principal component analysis (PCA) is then employed to extract principal components in statistics space. At last, the pattern of snapshot dataset is recognized by measuring the similarity of principal components derived from statistics snapshot dataset and statistics historical fault dataset. The effectiveness of suggested SPASF based approach is verified through a case study on continuous stirring tank reactor (CSTR) by the means of recognizing fault patterns of snapshot datasets.

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T2 and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.

Utilization of Classical Scaling Technique in Sustaining Fault Detection Performance in Process Monitoring


 
 
 
 Multivariate Statistical Process Monitoring (MSPM) fundamentally adopts the conventional Principal Component Analysis (cPCA) as the main platform for data compression. The main challenge though, the association nature of most industrial process variables are highly non-linear. As a result, the risks of applying the conventional approach of MSPM within this context may include sluggish or failed in detection, misinterpretation of signals, incorrect fault diagnosis and also inflexible as well as insensitive to changing of operating modes. In addressing the issue, this paper introduces new sets of monitoring parameters i.e. Sm2, Sr2 and Sr3, which have been derived within the frameworks of Classical Scaling (CMDS) and Procusters Analysis (PA) methods. The overall fault detection performance that applied based on the Tennessee Eastman Process (TEP) cases show that the Sr3 can detect the faults particularly for abnormal events number 3, 9, 15 and 19 in higher rate compared to the cPCA-MSPM system. This proves that the new monitoring statistics work effectively in avoiding missed detection during monitoring which cannot be addressed effectively by the traditional monitoring system.
 
 
 

Spherical coordinate-based kernel principal component analysis

This paper proposes a spherical coordinate-based kernel principal component analysis (PCA). Here, the kernel function is the nonlinear transform from the Cartesian coordinate system to the spherical coordinate system. In particular, first, the vectors represented in the Cartesian coordinate system are expressed as those represented in the spherical coordinate system. Then, certain rotational angles or the radii of the vector are set to their corresponding mean values. Finally, the processed vectors represented in the spherical coordinate system are expressed back in the Cartesian coordinate system. As the degrees of the freedoms of the processed vectors represented in the spherical coordinate system are reduced, the dimension of the manifold of the processed vectors represented in the Cartesian coordinate system is also reduced. Moreover, since the conversion between the vectors represented in the Cartesian coordinate system and those represented in the spherical coordinate system only involves some elements in the vectors, the required computational power for the conversion is low. Computer numerical simulation results show that the mean squares reconstruction error via the spherical coordinate-based kernel PCA is lower than that via the conventional PCA. Also, the required computational power is significantly reduced.

Fault detection and diagnosis in water resource recovery facilities using incremental PCA.

Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.

Specificities of small cell neuroendocrine prostate cancer: Adverse prognostic value of TTF1 expression

ObjectivesTo determine whether small cell neuroendocrine prostate cancers (NEPCa) emerging after anti-androgen treatments are different from the rarest cases diagnosed de novo, and to identify effective predictive markers. Material and methodsThe expression of neuroendocrine markers, androgen receptor (AR) and androgen-regulated genes, as well as markers of aggressiveness, were analyzed by immunohistochemistry on a tissue microarray containing samples of 30 sNEPCa, either pure or admixed with conventional PCa, and including 14 cases diagnosed de novo and 16 cases subsequent to prior androgen deprivation. ResultsChromogranin A is a better marker of NE differentiation than synaptophysin in post-treatment NEPCa, with 94% and 44% of positive tumors, respectively, while both markers are equally expressed in de novo cases. Despite the acquisition of a NE phenotype, more than half of NEPCa expressed AR and the androgen-regulated gene NKX3.1, more frequently in cases admixed with conventional PCa. TTF1 staining, present in half of NEPCa, was associated with loss of androgen-regulated genes and with markers of aggressiveness, including increased proliferation, Zeb1 expression and PTEN loss. In multivariate analysis, only TTF1 expression was significantly associated with shorter overall survival. ConclusionThese results suggest the persistence of androgen signaling in a number of NEPCa cases, and the interest of TTF1 staining as a predictive biomarker.

Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection Technique.

Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. The availability of high-dimensional medical image data during diagnosis places a heavy computational burden and a suitable pre-processing step is required for lower- dimensional representation. The storage requirement and complexity of image data are also a concern. To address this concern, the random projection technique (RPT) is widely used as a multivariate approach for data reduction. This study mainly focuses on T1-weighted MRI image clustering for brain tumor segmentation with dimension reduction by using the conventional principal component analysis (PCA) and RPT. Two clustering algorithms, K-means and fuzzy c-means (FCM) were used for brain tumor detection. The primary study objective was to present a comparison of the two clustering methods between MRI images subjected to PCA and RPT. In addition to the original dimension of 512 × 512, three other image sizes, 256 × 256, 128 × 128, and 64 × 64, were used to determine the effect of the methods. In terms of average reconstruction, Euclidean distance, and segmentation distance errors, the RPT produced better results than the PCA method for all the clustered images from clustering techniques. According to the values of performance metrics, RPT supported fuzzy c-means in achieving the best clustering performance and provided significant results for each new size of the MRI images.

New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln

Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in industrial process monitoring. It intends to promptly detect and identify abnormalities and enhance the reliability and safety of the processes. Kernel Principal Component Analysis (KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its ability in monitoring nonlinear systems. However, KPCA suffers from high computing time and large storage space when a large-sized training dataset is used. So, extracting and selecting the more relevant observations could provide a good solution to high computation time and memory requirements costs. In this paper, a new Reduced KPCA (RKPCA) approach is developed to address that issue. It aims to preserve one representative observation for each similar and selected Euclidean distance between training samples. Afterwards, the obtained reduced training dataset is used to build a KPCA model for FDD purposes. The developed RKPCA scheme is tested and evaluated across a numerical example and an actual involuntary process fault and various simulated sensor faults in a cement plant. The obtained results show high monitoring performance with highest robustness to false alarms and maximum fault detection sensitivity compared to conventional PCA, KPCA and other well-established RKPCA techniques. Furthermore, the unified contribution plot method demonstrates superior potentials in identifying faulty variables.

Multilinear Plus Sparse Based Tensor Completion for Long-Term Operating Large-Scale and Heterogeneous Sensor Networks

Data integrity and correctness are essential for many internet of things applications. This work addresses the data completion problem for long-term operating, large-scale and heterogeneous wireless sensor networks (WSNs), using tensor completion technique. In most previous works, the data models only involve single temporal and single spatial dimensions, ignoring the higher-order intrinsic structures. Besides, most existing schemes use low-rankness and sparsity in a fixed domain to describe the data correlation, but neither can fit well with all the temporal, spatial and attribute correlation. To address these issues, we first propose a higher-order tensor model for data from WSNs, namely multilinear plus sparse (MPS) model, which has multiple temporal, multiple spatial and one attribute modes. The core idea is to describe the temporal-spatial-attribute correlation by the sparsity in the multilinear transform domain. Appropriate sparsifying transforms can be flexibly determined according to the specific natures of each mode. For the temporal and spatial modes, analytical transforms are adopted considering computational efficiency and addressing continuity feature. For the attribute mode, we utilize MPS to propose a robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm based principle component analysis (robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA) algorithm to adaptively learn the sparsifying transform for various heterogeneous networks. Finally, based on MPS and the learned transform, we develop an efficient tensor completion algorithm using alternating direction method of multipliers, namely MPS-TC. The experimental results on real-life WSN data verify that the proposed robust ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCA algorithm can learn an attribute transform more robustly than the conventional PCA and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PCA algorithms; and the proposed MPS-TC algorithm outperforms the state-of-the-art tensor completion algorithms in terms of signal-to-noise ratio and root mean square error.

Derivation of a Dynamic Market Risk Signal Using Kernel PCA and Machine Learning

Kernel principal component analysis (PCA) is an extension of the conventional PCA method that employs a kernel transformation whereby hidden patterns in possibly multidimensional data may be detected and extracted more explicitly. In this article, the author applies the method of kernel PCA to a currency prediction case study and derives an aggregate market signal. It is observed that this signal has desirable information-compression properties and may be used as a predictive risk indicator in the return prediction models. Used alongside common drivers of exchange rates, a kernel PCA signal enhances in-sample and out-of-sample risk-adjusted performance across a range of machine learning strategies. In particular, the author observes that a kernel PCA signal remains robust and predictive during volatile market conditions. The kernel PCA signal may be used as a machine learning feature to inform and support data-driven risk management strategies. TOPICS:Big data/machine learning, derivatives Key Findings • This article develops a risk signal based on the method of kernel PCA. • Kernel PCA conveys desirable information on market dynamics. • Currency machine learning strategies benefit from this kernel PCA feature.