Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

Automatic ECG beat classification plays an important role in detecting cardiac disease. In this paper, we propose an automatic recognition model for ECG signals based on discrete wavelet transform (DWT), principal component analysis (PCA), kernel principal component analysis (KPCA), and adaptive kernel principal component analysis (AKPCA). We extracted different ECG features using DWT, PCA, KPCA, and AKPCA, respectively. These features were combined and used as support vector machine (SVM) input to classify the ECG. ECG records taken from the MIT-BIH arrhythmia database are selected to test the proposed method. The following five heartbeat types were classified using this method: normal beats (N), premature ventricular beats (V), right bundle branch block beats (R), left bundle branch block beats (L), and premature atrial beats (A). The sensitivity, accuracy, precision, and specificity reached 99.95%, 99.86%, 99.53%, and 99.70%, respectively. These results indicate the proposed method is reliable and efficient for ECG beat classification.

Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA

Composts are complex organic systems that undergo batch fermentation processes. Traditional monitoring of such processes is usually based on measuring important chemical (physical) laboratory parameters but the common trend includes using more rapid and non‐destructive methods like near‐infrared (NIR) spectroscopy. A lab‐scale designed (simplex mixture) experiment with nine compost batches, including three repeated centre point batches, was monitored over 5 weeks by NIR spectroscopy (900–1700 nm) and by wet chemical and physical measurements: pH, energy content, moisture content, NH3/NH and temperature. The data were organized in three‐way data arrays and different three‐way methods were used for analysis: (1) PARAFAC, (2) Tucker3 and (3) PARAFAC2. The present paper stresses the advantages and the possibilities of three‐way methods compared to traditional two‐way analysis methods such as principal component analysis (PCA). Two‐way methods have a tendency to mix variables and produce, from a parsimony point of view, more complex models which are hard to interpret. The results from the three‐way methods reproduced the mixture triangle, gave common time profiles (PARAFAC and Tucker3) for all compost batches and rate constants (half‐lives) could be calculated: 6.9 days for the PARAFAC loadings from the chemical/physical parameters and between 6 and 10 days for the PARAFAC loadings from the NIR data. PARAFAC2 includes the possibility of getting individual time profiles for each compost batch. The results show that chemical/physical data and the NIR data give similar interpretations. The conclusion is that three‐way methods can be used to monitor composts batches over time. Copyright © 2011 John Wiley & Sons, Ltd.

Adaptive sparse principal component analysis for enhanced process monitoring and fault isolation

In this paper, a novel nonlinear process monitoring and fault detection method based on kernel ICA is proposed. The Kernel ICA method is a two-phase algorithm, KPCA first spheres data and makes the data structure become as linearly separable as possible using an implicit nonlinear mapping determined by kernel. Then ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the FCCU simulated process indicates that the proposed process monitoring method based on Kernel ICA can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA or KPCA.

Batch or semi-batch processes have been utilized to produce high-value-added products in the biological, food, semi-conductor industries. Batch process, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes than that of continuous processes, due to the common natures of batch process: non-steady, time-varying, finite duration, and nonlinear behaviors. The lack of exact monitoring model in most batch processes leads that an operator cannot identify the faults when they occurred. Therefore, effective techniques for monitoring batch process exactly are necessary in order to remind the operator to take some corrective actions before the situation becomes more dangerous.

Multivariate allometric coefficients of red-spot prawn sampled from the adjacent waters of Tashi, Keelung, Taichung and Kaohsiung were estimated and compared to examine whether (1) allometry can be used to elucidate the genetic variation among groups and (2) the assumption that those groups compared share a common allometric pattern in some multivariate statistic method, e. g. Burnaby's method and shearing principal component analysis (PCA) for size correction, or common PCA and multiple-group PCA for multiple-group ordination, can be satisfied. The specimens were sampled during November and December in 1995. A total of 11 measurements was made for each individual. The first eigenvector extracted from the covariance matrix of log-transformed data was used to reflect the multivariate allometric coefficients. The bootstrap method was used to assess the accuracy of estimates. The dendrogram of four areas was constructed by unweighted pair-group method with arithmetic means (UPGMA) by using the angles between four first eigenvectors by sex. The results show that four areas were clustered into two distinct groups for each sex, one group included Tashi, Keelung and Kaohsiung; the other included Taichung. The hypothesis of one common multivariate allometric pattern was clearly rejected. This indicates that some multivariate statistic methods should not be used with out testing this assumption. The geographic clusters are different from the one derived from shape analysis, so the examination in genetic variation among populations by multivariate variate allometric comparison may be not suitable for this species. Different water masses in winter and flow patterns of coastal current around sampling areas may be the main factors to result in the differences of allometric coefficients between two clusters.

The practical application of traditional data‐driven techniques for process monitoring encounters significant challenges due to the inherent nonlinear and dynamic nature of most industrial processes. Aiming at the problem of nonlinear dynamic process monitoring, a novel fault detection method based on dynamic kernel principal component analysis combined with weighted structural difference (DKPCA‐WSD) is proposed in this paper. Initially, the proposed method leverages a sophisticated nonlinear transformation to project the augmented matrix of the original input data into a high‐dimensional feature space, thereby facilitating the establishment of a DKPCA model. Subsequently, the WSD statistic is computed, utilizing a widely known sliding window technique, to quantify the mean and standard deviation differences across data structures. Ultimately, the WSD statistic is utilized for fault detection, completing the process monitoring task. By integrating the capability of DKPCA to capture nonlinear dynamic characteristics with the effectiveness of the WSD statistic in mitigating the impact of non‐Gaussian data distributions, DKPCA‐WSD significantly enhances the monitoring performance of traditional DKPCA in nonlinear dynamic processes. The proposed method is evaluated through a numerical case exhibiting nonlinear dynamic behaviors and a simulation model of a continuous stirred tank reactor. A comparative analysis with conventional methods, including principal component analysis (PCA), dynamic principal component analysis, KPCA, PCA similarity factor (SPCA), DKPCA, and moving window KPCA (MWKPCA), demonstrates that DKPCA‐WSD outperforms traditional fault detection techniques in nonlinear dynamic processes, offering a substantial improvement in monitoring performance.

Low field \(^{1} \)H Nuclear Magnetic Resonance (LF NMR) Spectroscopy is an efficient tool to capture a research sample’s content and purity. LF NMR is employed for the qualitative analysis of Edible oils that are available in Indian Market. Edible oils used for the study include Coconut, Groundnut, Olive, Mustard, Rice bran, and Soyabean oil. Principal Component Analysis (PCA) is used to build a model initially that could classify the oils based on their chemical composition. This model built using PCA could capture 96% of total variance in data. Linear Discriminant Analysis (LDA) was used to build a model that could classify with 100% accuracy. The results provide successful proof for detecting adulteration and further classification of edible oils using Low field \(^{1} \)H NMR Spectroscopy in conjunction with Multivariate Statistical methods such as PCA and LDA.

Large-scale search method for locating and identifying fugitive emission sources in petrochemical processing areas

As a valid statistical tool, principal component analysis (PCA) has been widely used in industrial process monitoring. But due to its intrinsic linear character, it performs badly in nonlinear process monitoring. Kernel PCA (KPCA) can extract useful information in nonlinear data. However KPCA-based monitoring is not suitable for on-line monitoring because of large calculation and much memory occupation. The paper introduces an on-line monitoring method based on KPCA and neural network (NN), where KPCA is used to extract nonlinear principal components (PCs) and then NN approximates the relationship between process data and nonlinear PCs. We can obtain nonlinear PCs by NN to compute the monitoring indices and then achieve the on-line monitoring. The case study shows the validity of the method.KeywordsNeural NetworkKernel Principal Component AnalysisMonitoring IndexMemory OccupationComplex Industrial ProcessThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Principal component analysis (PCA) is a favorite tool in chemometrics for data compression and information extraction. PCA finds linear combinations of the original measurement variables that describe the significant variations in the data. However, it is well-known that PCA, as with any other multivariate statistical method, is sensitive to outliers, missing data, and poor linear correlation between variables due to poorly distributed variables. As a result data transformations have a large impact upon PCA. In this regard one of the most powerful approaches to improve PCA appears to be the fuzzification of the matrix data, thus diminishing the influence of outliers. In this paper we discuss a robust fuzzy PCA algorithm (FPCA). The new algorithm is illustrated on a data set concerning interaction of carbon-hydrogen bonds with transition metal-oxo bonds in molybdenum complexes. Considering, for example, a two component model, FPCA accounts for 97.20% of the total variance and PCA accounts only for 69.75%.

In process engineering, there are often states in which the local distribution of fluids and solids and their temporal and spatial concentration represent dominant process‐relevant parameters. This article is dedicated to the monitoring of such processes, e.g., drying, moistening, and crystal structure formation, which requires the use of both space‐resolved and molecule‐specific measurement methods. A non‐contact measuring procedure is the near‐infrared (NIR) image analysis with an indium gallium arsenide (InGaAs) sensor. The application possibilities and the potential of this inline measurement method are presented and explained.

The article is based on a research program aimed to identify strong and weak aspects of the implementation of the decree DM 509/1999 in the Italian university system and to evaluate whether the characteristics of the decree DM 70/2004 are able to respond to the critical issues that have emerged. The context of analysis is the University of Rome «Sapienza», chosen because of the institution’s numerous and composite dimensions covering all branches of scientific disciplines. From a substantive point of view, the phenomena of dropping-out, of delay in the course of studies and of the small numbers of graduations were analyzed using administrative data. From a methodological point of view, students’ careers were analyzed through multidimensional longitudinal models. The basic assumption of the research design is that a longitudinal perspective offers a more exact and detailed overview of university careers, in light of their nature of processes. The monitoring of such processes at short intervals of time (a semester) allows a comprehensive reconstruction of events/incidents potentially significant for the final outcome.

Investigations of inhomogeneous dynamics in industrial-scale bioreactors can be realized in laboratory simulators. Such studies will be improved by on line observation of the growth of microorganisms and their product synthesis at oscillating substrate availability which represents the conditions in industrial-scale fed-batch cultivations. A method for the kinetic monitoring of such processes, supported by on line measurements accessible in industrial practice, is proposed. It consists of a software sensor (SS) system composed of a cascade structure. Process kinetics are simulated in models with a structure including time-varying yield coefficients. SSs for measured variable kinetics have classical structures. The SS design of unmeasured variables is realized after a linear transformation using a logarithmic function. For these software sensors, a tuning procedure is proposed, at which an arbitrary choice of one tuning parameter value that guarantees stability of the monitoring system allows the calculation of the optimal values of six parameters. The effectiveness of the proposed monitoring approach is demonstrated with experimental data from a glucose-limited fed-batch process of Bacillus subtilis in a laboratory two-compartment scale down reactor which tries to mimic the conditions present in industrial scale nutrient-limited fed-batch cultivations.