Statistical Process Control Techniques Research Articles

Quality management of head and neck patient treatments using statistical process control techniques.

The treatment, planning, simulation, and setup of radiotherapy patients contain many processes subject to errors involving both staff and equipment. Cone-beam-CT (CBCT) provides a final check of patient positioning and corrections based on this can be made prior to treatment delivery. Statistical Process Control (SPC) techniques are used in various industries for quality management and error mitigation. The utility of SPC techniques to monitor process and equipment changes in our Head and Neck patient treatments was assessed by application to CBCT results from a quality-focused longitudinal study. Individuals and moving range (XmR) as well as exponentially-weighted moving average (EWMA) techniques were explored. The SPC techniques were sensitive to process changes and trends over the 12 years of data collected. A reduction in the random component of patient setup errors needing correction was observed. Systematic components of error remained more stable. An uptick in both datasets was observed correlating with the COVID-19 pandemic. Process control limits for use in prospective process monitoring were established. Challenges that arose from using SPC techniques in a retrospective study are outlined.

Statistical Process Control in Service Industries: Unveiling the keys to quality Management

In the service industries, maintaining a consistent level of quality is crucial for both client satisfaction and competitive advantage. Statistical Process Control (SPC) provides a systematic approach to monitoring and controlling processes, ensuring that quality criteria are met and maintained. This abstract will examine the application of SPC in several service industries and how it has contributed to raising the bar for process efficiency and service quality. The use of statistical process control (SPC) techniques is on the rise in the service industry, despite the methodologies' traditional association with manufacturing. According to this study, SPC is highly beneficial for service industries including healthcare, banking, and hospitality. We demonstrate the usefulness of SPC in identifying variations, reducing errors, and improving service quality through the examination of case studies and real data. Based on the findings, SPC is useful for making decisions in real-time, which opens the door to proactive management of service quality. It promotes a growth mindset and operational excellence as well. The research emphasizes the importance of training and the dedication of both personnel and management in order to successfully integrate SPC into service operations. The potential ways in which SPC could transform service sectors are outlined in this overview. Customer satisfaction and company productivity are both enhanced as a result of the systematization and data-driven approach to quality management that is promoted.

Electronic oral health surveillance system for Egyptian preschoolers using District Health Information System (DHIS2): design description and time motion study

BackgroundEarly childhood caries (ECC) is a major global health issue affecting millions of children. Mitigating this problem requires up-to-date information from reliable surveillance systems. This enables evidence-based decision-making to devise oral health policies. The World Health Organization (WHO) advocates the adoption of mobile technologies in oral disease surveillance because of their efficiency and ease of application. The study describes developing an electronic, oral health surveillance system (EOHSS) for preschoolers in Egypt, using the District Health Information System (DHIS2) open-source platform along with its Android App, and assesses its feasibility in data acquisition.MethodsThe DHIS2 Server was configured for the DHIS2 Tracker Android Capture App to allow individual-level data entry. The EOHSS indicators were selected in line with the WHO Action Plan 2030. Two modalities for the EOHSS were developed based on clinical data capture: face-to-face and tele/asynchronous. Eight dentists in the pilot team collected 214 events using modality-specific electronic devices. The pilot’s team's feedback was obtained regarding the EOHSS's feasibility in collecting data, and a time-motion study was conducted to assess workflow over two weeks. Independent t-test and Statistical Process Control techniques were used for data analysis.ResultsThe pilot team reported positive feedback on the structure of the EOHSS. Workflow adaptations were made to prioritize surveillance tasks by collecting data from caregivers before acquiring clinical data from children to improve work efficiency. A shorter data capture time was required during face-to-face modality (4.2 ± 0.7 min) compared to telemodality (5.1 ± 0.9 min), p < 0.001). The acquisition of clinical data accounted for 16.9% and 21.1% of the time needed for both modalities, respectively. The time required by the face-to-face modality showed random variation, and the tele-modality tasks showed a reduced time trend to perform tasks.ConclusionsThe DHIS2 provides a feasible solution for developing electronic, oral health surveillance systems. The one-minute difference in data capture time in telemodality compared to face-to-face indicates that despite being slightly more time-consuming, telemodality still shows promise for remote oral health assessments that is particularly valuable in areas with limited access to dental professionals, potentially expanding the reach of oral health screening programs.

Evaluation of Coal Supplied to Soma Thermal Power Plant (Türkiye) with Statistical Process Control Techniques

Statistical Process Control (SPC) is a method developed to ensure that production activities are carried out inaccordance with predetermined quality specifications and to minimize the production of defective products/goods bylargely preventing non-standard production. Various scientific methods are used when the calorific values of coal fed tothermal power plants are examined in terms of quality. In this study, the calorific values and other specifications of thecoal fed to the Soma Thermal Power Plant (Manisa/Türkiye) and whether the process is under control or not are examinedby considering the two-year data for tracking and monitoring the process with the Statistical Process Control (SPC)method, taking into account both the design conditions of the power plant and the production parameters. The adequacyof the processes for which stability analysis was performed was evaluated in the next stage. When the two-year controlcharts (

Integrating SERVQUAL and Statistical Process Control Techniques for Service Quality Improvements in a University's Registrar Office

Integrating SERVQUAL and Statistical Process Control Techniques for Service Quality Improvements in a University's Registrar Office

Monitoring of high-dimensional and high-frequency data streams: A nonparametric approach

ABSTRACT High-dimensional and high-frequency data streams (HHDS) have become widely available with the prevalence of data-acquisition technologies. Because HHDS have the characteristics of high dimensionality and temporal correlation, traditional statistical process control (SPC) techniques cannot be used directly to address the problem of monitoring HHDS. Most extant monitoring methods commonly assume that the process observations are independent or can be described by some parametric models and face the challenge posed by high-dimensional data. Little research has been conducted on the robust monitoring of HHDS that can simultaneously accommodate high dimensions and high frequencies. Therefore, in this paper, we propose a novel nonparametric approach for monitoring HHDS, based on data decorrelation, dimension reduction, and SPC. Specifically, process observations are first sequentially decorrelated and standardized using Cholesky decomposition, and then the k -nearest neighbors classification algorithm is applied to transform uncorrelated high-dimensional data into one-dimensional data. Finally, the traditional cumulative-sum procedure is used for online monitoring, based on an empirical log-likelihood ratio test. Numerical studies have shown that our monitoring scheme is sufficiently reliable and efficient for the online monitoring of HHDS. An illustrative example about the Dow Jones 30 industrial stock prices is presented to further validate the proposed approach.

Monitoring of group-structured high-dimensional processes via sparse group LASSO

In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their dependencies and relevance. The features that have similar characteristics or behaviors in the process operation can be categorized into multiple groups. Thus, when a few quality characteristics in the process change, it is highly probable that the process shift would have occurred in a few relevant groups. Recently, several advanced statistical process control techniques are developed to monitor the changes in high-dimensional processes under sparsity. However, monitoring schemes that utilize the grouped pattern of the quality characteristics are sparse. This paper proposes a new method to monitor the high-dimensional process when the grouped structure of the process data is observed. The proposed method identifies the potentially changed groups and individual variables within the groups based on a modified sparse group LASSO (MSGL) model. Then, a monitoring statistic is obtained using MSGL-based likelihood function to test abnormality of the process. Extensive numerical studies are conducted to demonstrate the effectiveness and efficiency of the proposed method. In addition, a real-life application of a liquefied natural gas process is presented to illustrate the proposed method.

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process

The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.

Copula deep learning control chart for multivariate zero inflated count response variables

With the increasing popularity of big data analysis, research on zero-inflated count data with the copula method has garnered significant attention because zero-inflated count data do not follow a normal distribution and have high correlation among variables. Within the domain of quality control, there has been limited emphasis on multivariate statistical process control (SPC) techniques that specifically address the challenge of multicollinearity within regression models for multivariate zero-inflated count responses. In this paper, we explain a computational challenge in handling big data with parametric generalized linear models, such as the zero-inflated Poisson model. This challenge motivates us to introduce a copula-based deep learning and neural network model involving multivariate zero-inflated count response variables with highly correlated explanatory variables. This approach builds upon existing deep learning and neural network models designed for univariate count responses, expanding them to encompass multivariate count response models through the integration of a copula regression framework. To evaluate the performance of our proposed methodology, we conduct a comparative analysis of accuracies using the zero-inflated Poisson model, univariate deep learning and neural network models, multivariate count response deep learning and neural network models, and our proposed copula deep learning and neural network models. This assessment involves employing metrics such as root mean square error (RMSE), weighted mean absolute percentage error (WMAPE), and mean absolute deviation (MAD). We include both copula-based asymmetrical zero-inflated simulated data and real-world data. We also propose a temporal dependence control chart for assessing the temporal dependence between bivariate zero-inflated count response variables. Our proposed copula deep learning and neural network temporal control charts can check time-varying dependence and outliers by leveraging the Shewhart statistical process control chart and t-copula ARMA-GARCH dynamic control correlation.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

STATISTICAL TECHNIQUES IN PRECISION METROLOGY, APPLICATIONS AND BEST PRACTICES

Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.&#x0D; Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.

STATISTICAL PROCESS CONTROL AS A TOOL FOR QUALITY IMPROVEMENT: A CASE STUDY IN DENIM PANT PRODUCTION

In this study, after defining the concepts of quality and quality control, the concept of statistical process control tools is examined. As a result of the data obtained from denim pants produced by a textile company operating in the ready-to-wear sector, defects were analyzed using statistical process control techniques. In this study, flowchart, control charts, pareto analysis, p-control diagram, cause-effect diagram, and grouping techniques were used. With the use of basic statistical process control tools, the most recurring defects were, and these defects were divided into subheadings for more detailed analysis. In this way, it was tried to prevent the repetition of defects by going down to the root causes of the detected defects. The causes of possible defects that may occur during production and solution suggestions were categorized. By going down to the basic causes of the detected defects and categorized them, defects were prevented by presenting and implementing solution suggestions. It is expected that the study with this different perspective will be an example of the sector and related fields as an alternative method. The aim of this study is to examine quality control practices in the garment industry, contribute to statistical control methods and problem-solving procedures, produce suggestions for problem-solving approaches, and disseminate improvement studies.

Implementation and Analysis of a Digital Twin for Propylene Glycol Production: Dynamic Simulation and Process Statistical Control

Purpose: Development of a dynamic simulation and analysis system for Propylene Glycol production through the hydrolysis of Propylene Oxide, utilizing market-available tools including a Digital Twin developed in the Unisim/Honeywell Simulation integrated to the PI System AVEVA/OSIsoft. This system aims to provide a realistic industrial environment for subsequent analysis and statical quality control. Theoretical framework: The manufacturing of Propylene Glycol involves complex reactive systems that require precise control and optimization. The integration of advanced simulation tools and statistical control charts, such as Shewhart Control Charts, is pivotal in monitoring and enhancing the quality of the process. This approach addresses the need for efficient process management in the chemical industry. Method/design/approach: The project employs a decoupled simulation structure, where the reactive system and the statistical control system operate simultaneously, exchanging information through the PI System supervisory software (AVEVA/OSIsoft) and a Python programming environment. The implementation of Shewhart Control Charts within the simulation provides real-time process quality monitoring. Results and conclusion: The outcomes demonstrate the feasibility of using a combination of software tools for daily industrial event simulation, as well as for training and acclimatizing engineers and operators. The analysis of statistical control charts allows for the detection of special cause variations, offering valuable insights for efficient process intervention, thereby enhancing the quality of both the process and the final product. Research implications: This study contributes to a deeper understanding of reactive systems in chemical engineering and the effective integration of simulation and statistical process control techniques. Originality/value: The development of a simulation framework that combines Digital Twin technology with statistical process control for the efficient and reliable production of Propylene Glycol.

Utilising routine clinical laboratory data to support quality improvement in health care: Application of a national acute kidney injury alert system as a proof of concept.

Acute kidney injury (AKI) is a global health issue known to cause avoidable harm and death. Improvement in its prevention and management is therefore considered an important goal for the health-care sector. The work here aimed to develop a tool which could be used to robustly and reliably measure, monitor, and compare the effectiveness of health-care interventions related to AKI across the Welsh NHS, a mechanism which did not exist previously. Using serum creatinine (SCr) as a biomarker for AKI and a validated national data-set collected from the all Wales Laboratory Information Management System, work involved applying Donabedian's framework to develop indicators with which to measure outcomes related to AKI, and exploring the potential of statistical process control (SPC) techniques for analysing data on these indicators. Rate of AKI incidence and 30-day AKI-associated mortality are proposed as valid, feasible indicators with which to measure the effectiveness of health-care interventions related to AKI. The control chart, funnel plot, and Pareto chart are proposed as appropriate, robust SPC techniques to analyse and visualise variation in AKI-related outcomes. This work demonstrates that routinely collected large SCr data offer a significant opportunity to monitor and therefore inform improvement in patient outcomes related to AKI. Moreover, while this work concerns utilisation of SCr data for improvement in AKI strategies, it is a proof of concept which could be replicated for other routinely collected clinical laboratory data, to improve the prevention and/or management of the conditions to which they relate.

TRENDING PERSPECTIVE IN EVALUATION OF INSPECTION CHARACTERISTICS OF PHARMACEUTICAL COMPOUND: COMPARATIVE STUDY OF CONTROL CHARTS

Background and objectives: The quality and efficacy of the pharmaceutical medicinal product are of paramount importance to the patients. Reproducibility and consistency of the properties of the dosage form start with the constituting ingredients either active or inactive. The present work provides an approach for the trending of one of the crucial inspection characteristics of a well-known excipient that is commonly used as a disintegrant of a water-insoluble synthetic cross-linked Polyvinyl Pyrrolidone (PVP). Establishing manufacturing criteria and quality limits should be assessed from the beginning level of the project using exploratory process-behavior plots. Materials and Methods: This study involved an assay of the initial 16 batches of the manufactured 1-ethenyl pyrrolidine-2-one expressed as a dried substance using a standard analysis method according to British Pharmacopoeia (BP). Exploratory Shewhart charts were plotted after preliminary distribution identification using Statistical Process Control (SPC) software. Based on the goodness of fit test results, the most appropriate assumed distribution was implemented in drawing the Individual/Moving Range (I/MR) control charts. Results: The goodness of fit study and probability plot showed that the most close-fitting to the data dispersion pattern was Johnson transformation to the normal spreading, smallest extreme value and Weibull distribution, respectively. While there were no alarms that could be detected from the process mean and the variability charts, further improvement is required from the supplier side to meet the benchmark minimum limit threshold, in addition, the process mean was not centered but shifted towards the Upper Control Limit (UCL). Conclusion: Implementation of SPC techniques is crucial in the modern competitive healthcare industry. Trending charts play a pivotal role in this perspective. They provide a quantitative estimation for the current situation and the actions needed for the future improvement of the monitored characteristic. Peer Review History: Received: 11 August 2023; Revised: 23 September; Accepted: 29 October, Available online: 15 November 2023 Academic Editor: Dr. Amany Mohamed Alboghdadly, Ibn Sina National College for Medical Studies in Jeddah, Saudi Arabia, amanyalboghdadly@gmail.com Received file: Reviewer's Comments: Average Peer review marks at initial stage: 6.5/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dr. Taiwo O Elufioye, University of Ibadan, Nigeria, toonitaiwo@yahoo.com Dr. Bilge Ahsen KARA, Ankara Gazi Mustafa Kemal Hospital, Turkey, ahsndkyc@gmail.com

The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics

The widespread implementation of computer technology has led to an increased use of Machine Vision Systems (MVS) for quality control in advanced manufacturing industries. The application of Statistical Process Control (SPC) techniques, especially control charts, is one of the most important and effective ways for fault detection in this field. Several statistical control charts have been extended to monitor images in the framework of MVS where the major aim is to improve the on-line detection ability, which is equivalent to avoid production of nonconforming products. In various industrial and non-industrial applications, control charts have been shown to be better able to detect anomalies in the underlying process(es) by integrating machine and ensemble learning techniques. However, in the field of image monitoring, approaches based on intelligent techniques have rarely been proposed. To bridge this gap and to improve the detection ability of control charts in monitoring image-based quality characteristics, this paper presents a novel control chart that combines machine learning, ensemble learning and image partitioning. In order to reach the maximum performance in Phase II SPC applications, specific designing and parameter tuning procedures are provided. The superiority of the proposed Partitioning Ensemble Control Chart (PECC) is verified by comparative analysis including conventional monitoring schemes by means of extensive simulations, in which different fault sizes and fault locations are applied to the images. Finally, a real MVS for quality control of O-rings is discussed to illustrate the practical application of the PECC.

Symmetry of gamma distribution data about the mean after processing with EWMA function

Statistical Process Control (SPC) plays a vital role in maintaining quality and reducing variability in manufacturing processes. Among SPC techniques, the Exponentially Weighted Moving Average (EWMA) stands out for its ability to detect small process shifts quickly, making it a valuable tool in ensuring product consistency and preventing quality issues. EWMA constructs control charts to monitor process mean shifts, tracks product/service quality by identifying variations, and monitors manufacturing process parameters for early detection of deviations and necessary adjustments. EWMA control chart has been proposed as an alternative to the Shewhart control chart. Sequential measurements are processed using the EWMA function before being placed on the control chart. One of the crucial concerns about the EWMA control chart is the asymmetry of the data around the mean. Although processing with the EWMA function reduces data skewness, the problem of asymmetric data may not be solved. The control chart is designed to leave in front of the upper control limit (UCL) α/2 of the data and behind the lower control limit (LCL) another α/2 of the data, and this does not occur in the case of symmetric data. α/2 represents the significance level for each tail in a two-tailed hypothesis test, indicating the probability of incorrectly rejecting the null hypothesis for each side of the distribution. Since many of the distributions in real life can be approximated by the Gamma distribution, the Gamma distribution was adopted in this study. The Monte Carlo simulation methodology was implemented to generate Gamma distributed data, process it with EWMA function and assess the skewness and kurtosis. The purpose of this paper is to evaluate the effect of EWMA parameters on the performance of the EWMA control chart. Moreover, it focuses on skewness and kurtosis reduction after data processing using the EWMA function. The findings help researchers and practitioners to select the best parameters. Further, the research investigates the effect of EWMA parameter on the shape of distribution.

An Outlier Detection Study of Ozone in Kolkata India by the Classical Statistics, Statistical Process Control and Functional Data Analysis

Air pollution is prevalent throughout the entire world due to the release of various gases such as NOx, PM, SO2, tropospheric ozone (O3), etc. Ground-stage ozone is the predominant issue in smog and is the product of the interplay between sunlight and emissions. The destructive impact on the health of the populace might also still occur in cities with noticeably clean air and where ozone levels hardly ever exceed safe limits. Therefore, the findings of small variations in air quality and the technique of regulating air contamination are thought-provoking. The study employs various techniques to effectively observe and assess strategies for detecting and eliminating outliers in ozone emissions from pollution episodes. This technique helps to describe the sources and exceedance values and enhance the value of monitoring the data. In this study, the data have some missing observations. The method of imputation, the classical statistical technique, the statistical process control (SPC) technique, functional data analysis (FDA), and functional process control help to fill in the data and detect outliers, trend deviations, and changes in ozone concentration at ground level. A comparison study is carried out using these three techniques: classical analysis, SPC, and FDA, and the results show how the statistical process control and functional data methods performed better than the classical technique for the detection of outliers and also in what way this methodology can enable an additional, comprehensive method of defining air pollution control measures and water pollution control measures.

APPLICATION OF STATISTICAL PROCESS CONTROL IN MECHANIZED CORN SEEDING

Several factors alter the quality of the mechanized sowing process of corn, and the use of the statistical process control (SPC) technique not only allows the identification of process limitation points but also ensures the correct functioning and conduction of the mechanized process, determining critical factors. that may decrease their performance. In view of the above, the present study aims to qualitatively evaluate, through this technique, the longitudinal distribution of corn seeds as a function of different stands and operational speeds during the sowing process. The experiment was carried out in a randomized block design (CBD) with three replications and in a split-plot design. Data were submitted to analysis of variance and when significant, analyzed using Tukey's test, with 5% significance. To carry out the statistical process control, mean and amplitude control charts were used to evaluate the effect of operational speed, seed distribution, deposition depth and plant germination. The results indicate that the use of statistical process control is an efficient tool to identify the behavior of the corn sowing process. Based on the results, speeds close to 4.5 km/h allowed an efficient sowing process, as it presented a higher percentage of normal spaces in the distribution of maize seeds.

New Anomaly Detection Method based on the Multivariate Generalized Pareto Distributions

The extreme events of floods, rainfalls, waves, and wind often cause serious damage. The observed data can be used to detect the sign of abnormal phenomena and can prevent the crucial damage. Anomaly detection techniques, the techniques of distinguishing between normal and abnormal cases from the data, have been one of the most used methods for detecting suspicious events from the collected data. Although many methods of the anomaly detection have appeared in the literature, almost all of them are based on multivariate statistical process control techniques and machine learning techniques. Recently, under the idea that information for suspicious and critical events is more likely to be involved in the largest or smallest values than values around the mean or median, the anomaly detection methods are based on the “Extreme Value Theory” (EVT), which is the statistical theory dealing with the largest or smallest data, attract the variety of areas. However, they can be applied to only univariate data since they have been based on the EVT for univariate data, and hence there has been little work for the multivariate anomaly detection based on the EVT. The multivariate anomaly detection method is important in applications because almost all data for anomaly detection are multivariate and can be misleading by applying univariate methods to multivariate data independently. In this paper, we propose the new anomaly detection method based on the multivariate EVT. The performance of the proposed methods is evaluated by the Monte Carlo simulation. To illustrate all the proposed methods of anomaly detection for multivariate data, we apply them to analyze the new real data of precipitation events in 2021. The numerical results show that the proposed method outperforms the existing methods.