• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.675-693
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• 2011

We compare three Techniques control systems with The Investigate Study on the relation between Multivariate SPC and Autoregressed Algorithm. We also investigate Autoregressed Algorithm with relevant EWMA, CUSUM, Shewhart chart, Precontrol chart and Process Capacity.

• Smart Structures and Systems
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• v.14 no.3
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.1-10
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• 2002

Process capability indices are widely used in industries and quality assurance system. In past years, process capability analysis have been used to characterize process performance on the basis of univariate quality characteristics. However, in actual manufacturing industrial, statistical process control (SPC) often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. Therefore, the analysis have to be required a multivariate statistical technique. This paper introduces to multivariate capability indices and then selects a multivariate process capability index incorporated both the process variation and the process deviation from target among these indices under the multivariate normal distribution. We propose a new multivariate capability index $MC_{pm}^+$ using quality loss function instead of the process variation and this index is compared with the proposed indices when quality characteristics are independent and dependent of each other.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.387-396
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• 2002

Batch processes are a significant class of processes in the process industry and play an important role in the production of high quality speciality materials. Examples include the production of semiconductors, chemicals, pharmaceuticals, and biochemicals. With on-line sensors connected to most batch processes, massive amounts of data are being collected routinely during the batch on easily measured process variables such as temperatures, pressures, and flowrates. In this paper, multivariate SPC charts for on-line monitoring of the progress of new batches are developed which utilize the information in the on-line measurements in real-time. We propose the formation of statistical model which describes the normal operation of a batch at each time interval during the batch operation. An on-line monitoring scheme based on the proposed method can handle both cross-correlation among process variables at any one time and auto-correlation over time. And the control limits for the monitoring charts are established from sound statistical framework unlike previous researches which use the external reference distribution. The proposed charts perform real-time, on-line monitoring to ensure that the batch is progressing in a manner that will lead to a high-quality product or to detect and indicate faults that can be corrected prior to completion of the batch. This approach is capable of tracking the progress of new batch runs, identifying the time periods in which the fault occurred and detecting underlying cause.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.1-11
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• 2018

Multivariate process control has become important in various applied fields. For instance, there are many situations in which the simultaneous monitoring of multivariate quality characteristics is necessary for the manufacturing industry. Despite its importance, its practical usage is not as convenient because it is difficult to identify the source of the out-of-control signal in a multivariate control chart. In this paper, we will introduce how to detect the source of the out-of-control by using confidence intervals for new observations, and will discuss the identification and interpretation of the out-of-control variable through simulation studies.

• Animal Bioscience
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• v.35 no.7
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• pp.1030-1038
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• 2022

Objective: Two experiments were conducted to evaluate the effect of enzyme complex (EC) on the standardized ileal digestibility (SID) of amino acids (AA) in corn gluten meal (60%) (CGM), soy protein concentrate (SPC), dried bovine plasma (DBP), and poultry offal meal (POM). Experiments I and II were conducted with broilers in the pre-starter (1 to 7 days of age) and starter (1 to 21 days of age) phases, respectively. Methods: The treatments consisted of a protein-free diet (PFD) containing feedstuffs either supplemented with EC (xylanase, amylase, and protease) or not. In Experiment I, a total of 360 one-day-old male Cobb-500 broiler chicks were randomly housed in 45 pens, resulting in five replicates with eight birds each, totalizing eight treatments and one PFD group. In Experiment II a total of 270 one-day-old male Cobb-500 broiler chicks were randomly housed in 45 pens, resulting in five replicates with six birds each, totalizing eight treatments and one PFD group. The PFD groups were used to assess the endogenous AA losses. The birds were slaughtered to collect the ileal content. Results: In the pre-starter phase, the SID of arginine, branched chain-aminoacids, glycine, serine, aspartate, and glutamic acid increased with EC addition. The EC improved the SID of arginine and glutamic acid of CGM; the SID of valine and cystine of SPC; the SID of leucine, glycine, and aspartate of POM and the SID of isoleucine of DBP. In the starter phase, the SID of isoleucine, phenylalanine and glycine increased in EC-supplemented diets. The EC improved the SID of isoleucine of DBP; the SID of phenylalanine of CGM and POM. The SID of AA of SPC was not influenced by the EC. Conclusion: The addition of an EC to broiler pre-starter and starter diets is efficient in increasing the SID of AA on SPC, POM, and DBP.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.39 no.spc1
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• pp.203-213
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• 2006

The spatial patterns of meiobenthic communities in deep-sea sediment were examined. Sediment samples for analyzing of meiobenthic community structure were collected using a remote operated vehicle (ROV), multiple corer TV grab at 20 stations at five sites. In all, 15 meiofauna groups were recorded. Nematodes were the most abundant taxon. Benthic foraminiferans, harpacticoid copepods, polychaetes, and crustacean naupii were also dominant groups at all sites. The total meiofauna density at the study sites varied from 49 to 419 ind./$10cm^2$. The maximum density was recorded at a site located in Challenger Deep in the Mariana trench where simple benthic foraminifera with organic walls flourish. These distinctive taxa seem to be characteristic of the deepest ocean depths. Active hydrothermal sediments contain up to 150 harpacticoid copepods per $10cm^2$ of sediment. In a inactive ridge sediments, devoid of macrofaunal organisms:, the abundance of harpacticoid copepods never exceeded 15 ind./$10cm^2$. Multivariate analysis (multidimensional scaling) revealed significant differences in community structure among the three regions; near an active hydrothermal vent, in the deepest ocean depths and at typical deep-sea bed sites.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.13-22
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• 2007

Fault detection of cycle-based signals is typically performed using statistical approaches. Univariate SPC using few representative statistics and multivariate analysis methods such as PCA and PLS are the most popular methods for analyzing cycle-based signals. However, such approaches are limited when dealing with information-rich cycle-based signals. In this paper, process fault defection method based on wavelet analysis is proposed. Using Haar wavelet, coefficients that well reflect the process condition are selected. Next, Hotelling's $T^2$ chart using selected coefficients is constructed for assessment of process condition. To enhance the overall efficiency of fault detection, the following two steps are suggested, i.e. denoising method based on wavelet transform and coefficient selection methods using variance difference. For performance evaluation, various types of abnormal process conditions are simulated and the proposed algorithm is compared with other methodologies.