Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes

This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed

An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability

The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.Comment: 19 pages, 9 figures, 6 table

Information and Control ICIC International c ⃝2011 ISSN

Abstract. The statistical process control (SPC) chart is effective in detecting proces

The blockage ratio effect to the spray performances

Nozzle sprays are used in wide range of application. The used of nozzle application is depend on the spray characteristics, by which to suit the particular application. This project studies the effect of the air blockage ratio to the spray characteristics. This research conducted into two part which are experimental and simulation section. The experimental was conducted by using particle image velocimetry (PIV) method, and ANSYS software was used as tools for simulation section. There are two nozzles were tested at 1 bar pressure of water and air. Nozzle A (with blockage ratio 0.316) and nozzle B (blockage ratio 1.000). Both of the sprays performances generated by the nozzles was examined at 9 cm vertical line from 8 cm of the nozzle orifice. The validation result provided in the detailed analysis shows that the trend of graph velocity versus distance gives the good agreement within simulation and experiment. From result, nozzle A generated a wider spray angle and higher water droplet velocity which are 31.41 degree and 37.317 m/s compared to nozzle B which has produced 27.13 degree of spray penetration angle and 16.49 m/s water droplet velocity. As a conclusion, blockage ratio has affected the spray system by increasing the velocity of air inside the spray system. This is happened at a condition of 1 bar air pressure

Menghilangkan Autokorelasi Pada Diagram Kontrol Shewhart Menggunakan Diagram Kontrol Residual Berdasarkan Model Extention Support Vector Regression

Kualitas merupakan faktor kunci yang mengarahkan kepada keberhasilan, pertumbuhan, dan daya saing bisnis. Kualitas juga merupakan salah satu faktor penting dalam pengambilan keputusan konsumen dalam pemilihan produk dan layanan. Guna meningkatkan kualitas produk dapat memanfaatkan beberapa cara, salah satunya adalah menerapkan statistical process control (SPC). Salah satu tool SPC yang paling banyak diterapkan adalah diagram kontrol yang berguna untuk mengetahui variansi dari proses. Diagram kontrol didasarkan pada asumsi bahwa data mengikuti distribusi normal dan tidak terdapat hubungan antara pengamatan yang berurutan (autokorelasi). Namun dalam proses industri kontinyu kebanyakan data bersifat autokorelasi. Agar bisa menggunakan diagram kontrol secara efektif, autokorelasi dalam data harus dihilangkan. Langkah yang dapat dilakukan untuk pengendalian kualitas pada data autokorelasi adalah dengan memetakan residual hasil pemodelan menggunakan metode time series pada diagram kontrol. Pada penelitian ini dikembangkan diagram kontrol residual berdasarkan model extention Support vector regression yaitu Least square support vector regression dan Genetic algorithm support vector regression untuk mengatasi kasus autokorelasi pada proses. Kriteria kebaikan model dalam penelitian ini menggunakan nilai Root Mean Square Error (RMSE). Semakin kecil nilai RMSE maka model yang digunakan semakin baik. Setelah dilakukan perhitungan menggunakan metode regresi, Support vector regression dan metode Extention support vector regression, metode yang paling baik adalah Genetic algorithm support vector regression berdasarkan nilai RMSE sebesar 1,554310 dan 0,5565. ========================================================================================================= Quality is a key factor that leads to business success, growth, and competitiveness. Quality is also an important factor in consumer decision making in the selection of products and services. In order to improve product quality can utilize several ways, one of them is apply statistical process control (SPC). One of the most widely applied SPC tools is the control chart which is useful for knowing the variance of the process. The control chart is based on the assumption that data follows a normal distribution and there is no relationship between successive observations (autocorrelation). But in the process of continuous industry most data are autocorrelation. In order to use the control chart effectively, autocorrelation in the data must be eliminated. Steps that can be done to control the quality of the autocorrelation data is to map the residual results of modeling using time series method in the control chart. In this research, the residual control charts are developed based on the extension support vector regression model that is Least square support vector regression and Genetic algorithm support vector regression to overcome the case of autocorrelation in the process. Criteria of model goodness in this research use Root Mean Square Error (RMSE). The smaller the value of RMSE then the model used the better. After calculation using regression method, Support vector regression and Extension support vector regression method, the best method is Genetic algorithm support vector regression based on RMSE value of 1.554310 and 0.5565

Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system

On-line recognition of abnormal patterns in bivariate autocorrelated process using random forest

It is not uncommon that two or more related process quality characteristics are needed to be monitored simultaneously in production process for most of time. Meanwhile, the observations obtained online are often serially autocorrelated due to high sampling frequency and process dynamics. This goes against the statistical I.I.D assumption in using the multivariate control charts, which may lead to the performance of multivariate control charts collapse soon. Meanwhile, the process control method based on pattern recognition as a non-statistical approach is not confined by this limitation, and further provide more useful information for quality practitioners to locate the assignable causes led to process abnormalities. This study proposed a pattern recognition model using Random Forest (RF) as pattern model to detect and identify the abnormalities in bivariate autocorrelated process. The simulation experiment results demonstrate that the model is superior on recognition accuracy (RA) (97.96%) to back propagation neural networks (BPNN) (95.69%), probability neural networks (PNN) (94.31%), and support vector machine (SVM) (97.16%). When experimenting with simulated dynamic process data flow, the model also achieved better average running length (ARL) and standard deviation of ARL (SRL) than those of the four comparative approaches in most cases of mean shift magnitude. Therefore, we get the conclusion that the RF model is a promising approach for detecting abnormalities in the bivariate autocorrelated process. Although bivariate autocorrelated process is focused in this study, the proposed model can be extended to multivariate autocorrelated process control

Detection and identification of mean shifts in multivariate autocorrelated processes: A comparative study

Master'sMASTER OF SCIENCE (MANAGEMENT

Monitoring and performance analysis of regression profiles

There are many cases in industrial and non-industrial sections where the quality characteristics are in the form of profiles. Profile monitoring is a relatively new set of techniques in statistical quality control that is used in situations where the state of product or process is presented by regression models. In the past few years, most research in the field of profile monitoring has mainly focused on the use of effective statistical charting methods, study of more general shapes of profiles, and the effects of violations of assumptions in profile monitoring. Despite several research on the application of artificial neural networks to statistical quality control, no research has investigated the application of neural networks in monitoring profiles. Likewise, there is no research in the literature on the process capability analysis in profile processes. The process capability analysis is to evaluate the ability of a process to meet the customer/engineering specifications and must be done in Phase I of profile monitoring. In a review study on profile monitoring, Woodall (2007) pointed out the importance of process capability analysis in profiles. In this research, we use artificial neural networks (ANN) to detect and classify shifts in linear profiles. Three monitoring methods based on ANN are developed to monitor linear profiles in Phase II. We compare the results for different shift scenarios with existing methods in linear profile monitoring and discuss the results. Furthermore, in this thesis, we evaluate the estimation of process capability indices (PCIs) in linear profiles. We propose a method based on the relationship between proportions of non-conformance and the process capability indices in the profile process. In most existing profile monitoring methods in the literature, it is assumed that the profile design points are deterministic (fixed) so they are unchanged from one profile to another one. In this research, we investigate the estimation of the PCI in normal linear profiles for different scenarios of deterministic and arbitrary (random) data acquisition schemes as well as fixed or linear functional specification limits. We apply the proposed method in estimating the PCI in a yogurt production process. This thesis also focuses on the investigation of the process capability analysis in profiles with non-normal error terms. In this study, we review the methods for estimating PCI in non-normal data and carry out a comprehensive comparison study to evaluate the performance of these methods. Then these methods are applied in the leukocyte filtering process to evaluate the PCI with effect of non-normality in a blood service section. In addition, we develop a new method based on neural networks to estimate the parameters of the Burr XII distribution, which is required in some of the PCI estimation methods with non-normal environments. Finally, in this research we propose five methods to estimate process capability index in profiles where residuals follow non-normal distributions. In a comparison study using Monte Carlo simulations we evaluate the performance of the proposed methods in terms of their precision and accuracy. We provide conclusions and recommendation for the future research at the end

A study of advanced control charts for complex time-between-events data

Ph.DDOCTOR OF PHILOSOPH