Statistical Monitoring Research Articles

Optimal constrained design of control charts using stochastic approximations

In statistical process monitoring, control charts typically depend on a set of tuning parameters besides its control limit(s). Proper selection of these tuning parameters is crucial to their performance. In a specific application, a control chart is often designed for detecting a target process distributional shift. In such cases, the tuning parameters should be chosen such that some characteristic of the out-of-control (OC) run length of the chart, such as its average, is minimized for detecting the target shift, while the control limit is set to maintain a desired in-control (IC) performance. However, explicit solutions for such a design are unavailable for most control charts, and thus numerical optimization methods are needed. In such cases, Monte Carlo-based methods are often a viable alternative for finding suitable design constants. The computational cost associated with such scenarios is often substantial, and thus computational efficiency is a key requirement. To address this problem, a two-step design based on stochastic approximations is presented in this paper, which is shown to be much more computationally efficient than some representative existing methods. A detailed discussion about the new algorithm’s implementation along with some examples are provided to demonstrate the broad applicability of the proposed methodology for the optimal design of univariate and multivariate control charts. Computer codes in the Julia programming language are also provided in the supplemental material.

Тенденции смертности от внешних причин населения Европейского Севера за 2010–2021 годы

The purpose of this research was to assess the status and identify the specific features of population mortality from external causes in the Arkhangelsk Region, Nenets Autonomous Area and Komi Republic for the period of 2010–2021. Materials and methods. Information about external cause mortality in the above regions for the period under study was obtained from the statistical monitoring databases of the Russian Federation – Unified Interdepartmental Statistical Information System – as well as from the official websites of the Main Directorate for Traffic Safety of the Ministry of Internal Affairs of Russia, and Federal State Statistics Service. Mathematicalstatistical and information-analytical methods were used in the paper. Results. In 2010–2021, the Arkhangelsk Region saw a steady downward trend in road traffic mortality; however, its average mortality rates remained at a higher level compared to the Nenets Autonomous Area and Komi Republic. During the period under study, the highest suicide rate among the regions was observed in the Nenets Autonomous Area: an average of 44 cases per 100,000 population annually, while in the Komi Republic and Arkhangelsk Region it was 30 % lower, i.e., 30.8 and 30.7 cases per 100,000 population, respectively. A better situation was observed in terms of homicide mortality, which decreased significantly between 2010 and 2021 in the regions under study. During the same period, mortality rates due to accidental alcohol poisoning dropped in all three regions; however, the average rate remained higher than the national average: 23.8, 12.2 and 26.1 cases per 100,000 population in the Arkhangelsk Region, Nenets Autonomous Area and Komi Republic, respectively. Dynamics of mortality from occupational injuries over the 12 years in the regions under study is insignificant and overall is similar to the national dynamics.

Process monitoring and fault diagnosis method combining bagging DPCA‐ICA with moving window Kolmogorov–Smirnov test

AbstractProcess monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%.

Does Central Statistical Monitoring Improve Data Quality? An Analysis of 1,111 Sites in 159 Clinical Trials

BackgroundCentral monitoring aims at improving the quality of clinical research by pro-actively identifying risks and remediating emerging issues in the conduct of a clinical trial that may have an adverse impact on patient safety and/or the reliability of trial results. This paper, focusing on statistical data monitoring (SDM), is the second of a series that attempts to quantify the impact of central monitoring in clinical trials.Material and MethodsQuality improvement was assessed in studies using SDM from a single large central monitoring platform. The analysis focused on a total of 1111 sites that were identified as at-risk by the SDM tests and for which the study teams conducted a follow-up investigation. These sites were taken from 159 studies conducted by 23 different clinical development organizations (including both sponsor companies and contract research organizations). Two quality improvement metrics were assessed for each selected site, one based on a site data inconsistency score (DIS, overall -log10P-value of the site compared with all other sites) and the other based on the observed metric value associated with each risk signal.ResultsThe SDM quality metrics showed improvement in 83% (95% CI, 80–85%) of the sites across therapeutic areas and study phases (primarily phases 2 and 3). In contrast, only 56% (95% CI, 41–70%) of sites showed improvement in 2 historical studies that did not use SDM during study conduct.ConclusionThe results of this analysis provide clear quantitative evidence supporting the hypothesis that the use of SDM in central monitoring is leading to improved quality in clinical trial conduct and associated data across participating sites.

Detecting outliers in the multivariate control charts for dispersion monitoring

AbstractOutlier detection is an important aspect of statistical process monitoring (SPM) because outliers affect the performance of control charts. SPM researchers study the negative impact of outliers on control charts for monitoring location parameters. However, there is little research on outlier detection in multivariate charts for monitoring process dispersion. This study aims to investigate the impact of outliers in multivariate control charts for monitoring covariance matrix of a process, and then to recommend techniques for detecting potential outliers present from Phase I samples. We propose a new multivariate dispersion chart that employs the determinant of logarithm of estimated covariance matrix as the monitoring statistic. Through Monte Carlo simulations, the results show how outliers from the first phase affect the overall performance of multivariate charts. The results also demonstrate that the minimum volume ellipsoid (MVE) estimator is effective in reducing the effect of outliers on the proposed control scheme than the other compared estimators.

Algorithm for identifying parameters of production function models using a neural network

The purpose of the work is to ensure the monitoring and reliability of statistical data used for the analysis and forecasting of the production development on the basis of generalized indicators that take into account the volume of resources used and the ways of their use. An algorithm for parametric identification of production functions models from time series of statistical data using generalized regression, which provides the best estimation of the parametric optimization error by the method of least squares, is proposed. In contrast to existing assessment methods, neural networks are used to build models, which significantly expand the technical capabilities of modeling and contribute to improving the accuracy of calculations through the use of neural network technologies. It is shown that in order to solve the problems of this class, it is advisable to use generalized regression neural networks with simple training modes and high modeling accuracy. As a result, it is possible to propose an algorithm for quantitative assessment of the production functions parameters, which consists in the construction of a neural model with its subsequent use to fit the trajectory of the production model of a given structure to the obtained data by means of recurrent estimates of the vector of the desired coefficients at a given initial approximation. The proposed algorithm is demonstrated by estimating the parameters of the Cobb-Douglas production function and the discrete-dynamic model of the consumption function according to the corresponding statistical series. The calculations are performed using the functions of the Neural Networks package of the MATLAB software environment. The algorithm is applicable for quantitative estimation of the production models parameters with complex logical-probabilistic relationships, as well as for obtaining numerical values of target indicators and indicators for assessing the inland water transport development by statistical series and monitoring.

The increase in the incidence of syphilis in the Russian Federation: foreign migrant citizens as a risk group for the spread of the disease

Relevance. The incidence of sexually transmitted infections (STIs) has a serious impact on the health and lives of children, adolescents and adults. Syphilis, like most STIs, is a socially significant disease, while among the factors influencing the spread of this infection, migration processes, including labor migration, occupy a special place.
 Aims. To study syphilis in foreign migrants in the Russian Federation and individual subjects of the state in recent years.
 Material and methods. A retrospective epidemiological analysis of the incidence of syphilis among the population of the Russian Federation and foreign migrants was performed. The data from Federal Statistical Monitoring Form No. 9 and No. 34 was used with reference to STIs incidence in 2011–2022 in Russia and in its regions.
 Results. Among foreign migrant citizens, the incidence rates were higher than the Russian average: 1.5–2.0 times before 2020, and 4 times in 2021 and 2022. The proportion of registered cases of syphilis among foreign citizens in 2021 was 36.4%, significantly exceeding this figure not only in 2020 (18.2%), but also in previous years (16.7–21.2%). An increase in the number of foreign citizens with syphilis was observed in 2021 in all federal districts of the Russian Federation, while the number of syphilis cases detected in this contingent of people varied significantly between different regions of the Russian Federation. The main share (98.1%) in the structure of syphilis detected in foreign citizens was latent forms of the disease.
 Conclusions. The high level of detection of syphilis in foreign migrant citizens and the predominance of latent forms of the disease in this population represent a potential epidemiological danger of the spread of infection. In connection with the current situation, it seems necessary to develop and implement permanent and controlled algorithms for monitoring STIs in risk groups, including foreign migrant citizens.

Integration of statistical process monitoring, maintenance and production planning for a multi-component system using the structural importance measure

Integration of statistical process monitoring, maintenance and production planning for a multi-component system using the structural importance measure

Integration of statistical process monitoring, maintenance and production planning for a multi-component system using the structural importance measure

Integration of statistical process monitoring, maintenance and production planning for a multi-component system using the structural importance measure

A Shape-Based Monitoring Method for Variable Trajectories of Finishing Mill Processes

As the most critical procedure in the hot strip mill process (HSMP), the performance of the finishing mill process (FMP) has a significant influence on the key performance indicators (KPIs). Considering that the multivariate statistical process monitoring (MSPM) methods and batch alignment methods (which are implemented in a point-to-point manner) cannot effectively handle the uneven-length and multiphase characteristics of FMP data, we propose a method to monitor the shape of the variable trajectories that are closely associated with KPIs. First, the historical FMP data characterized by different grades and thickness is utilized to build monitoring models based on the Shape-Based Distance (SBD) to obtain two statistics, i.e. the SBDs and the corresponding offsets when aligned with the central trajectories. Then, by comparing the statistics of the new coming trajectories with the control limits obtained from the training set, the new coming batches can be monitored. In the end, the proposed method is tested on the real FMP dataset to validate its effectiveness.

The evolution of personnel potential of Oblast leading health care institution

The state health care policy in Russia is directed at improving medical care quality and population satisfaction with it. The functioning of medical organizations beloning to national health care system has determinative impact on population health. The increasing of level of organization of their functioning is imposible without target manpower policy, upgrading qualification and professionalism of health care workers. The purpose of the study was to analyze personnel potential of Oblast clinical hospital, as one of leading health care institutiond of the Astrakhan Oblast. The methodologies of statistical, logical and system analysis were applied. The study used method of continuous sampling and was carried out in 2012-2021. The information sources included forms of the Federal statistical monitoring № 30 "Information on the medical organization" and № 17 "Information on medical and pharmaceutical workers". The absolute and mean values, extensive parameters modeling structure and methods of dynamic series construction were applied. The study revealed positive dynamics of increasing of number of established posts on 33.7%. The number of physical persons of hospital medical personnel (the basic increase made up to 40%) increased. It resulted in decreasing of combining jobs coefficient and in increasing of staff efficiency employment. The women consisted 65.2% of all working physicians. The mean age made 43.2 years among phycians and 41.7 years among paramedical personnel. The ratio 1.5:1 of paramedical personnel to physicians in staff list is negative factor. Alongside, ratio of physical persons of paramedical personnel and physicians decreases. It testifies necessity of increasing both established posts and involvement of more physical persons to work on paramedical personnel rates.

МЕТОДИЧЕСКАЯ ОСНОВА ДЛЯ РАСЧЕТА ИНДИКАТОРОВ ТЕНЕВОЙ ЭКОНОМИКИ В РОССИЙСКОЙ ФЕДЕРАЦИИ

A qualitative analysis of the current economic situation at the regional level is possible due to the available approaches to identifying the shadow economy’s subjects. Even if successfully applied to a specific subject, the research model can quickly lose its relevance due to technological progress, updates to the legal framework, and changes in the general approach to managing business entities. Most enterprises combine legal and illegal ways of conducting financial activities and focus on indicative markers of a particular industry or region, which demonstrates the other cause of the reduced effectiveness of previously used calculations. The study reviews the methods of statistical monitoring of the operation of enterprises and entities that allow for detecting signs of shadow economy activity, identifying the most problematic industries in the region, and assessing the damage to its well-being. The indicators are established based on the analysis of officially published statistical data, which makes it possible to determine the proportion of enterprises engaged in shadow activity. A review of existing models will provide a correct approach suitable for the given research conditions and the identification of subjects applying tax avoidance schemes, including those using illegal workers’ labor. Successful operation of models will ensure the correct scientific approach and create the foundation for obtaining high-quality analysis findings.

Частный музей в пространстве культуры: проблемы определения и статистики

Private museums play an important role in the modern cultural space of Russia; their activities make a significant contribution to the preservation and actualization of cultural heritage. The study of private museums as a special typological group that developed in the post-Soviet period has a number of terminological and statistical issues. In the context of the legal insecurity of the name “museum” and the lack of licensing of museum activities, many private organizations call themselves museums, but at the same time they do not meet the definition of a museum in the Russian legislation and the definition of the International Council of Museums (ICOM). Despite carrying out some functions of a museum and practicing some types of activities characteristic of museums, they are not museums, but museum-type institutions — creative workshops, recreational or play centers, shops, etc. There is no term “private museum” in the legislation, and the measurement uncertainty of this category of institutions remains in the statistics. An analysis of the statistical reports of a number of European countries shows that the category of “private museum” in different countries has different content. In Russia, due to the peculiarities of conducting statistical monitoring of the museum sphere, private museums are not taken into account by means of state statistics. The problem of terminological uncertainty of a private museum also exists in museology: there is no single interpretation of this group of institutions, and its specific features and essential characteristics need further study and definition.

ОСНОВНЫЕ ПОКАЗАТЕЛИ ПОДГОТОВКИ КАДРОВ В СИСТЕМЕ ПРОФЕССИОНАЛЬНОГО ОБРАЗОВАНИЯ РЕГИОНОВ АЗРФ

Secondary vocational education and higher education programs in the federal subjects of the RF Arctic Zone are being offered in all fields of study and for the majority of the general specialty groups and educational profiles. Systematic research and analysis of the main indicators of personnel training in the vocational and higher education system of the federal subjects of the RF Arctic Zone is one of the mechanisms for ensuring the implementation of the state policy of the Russian Federation in the Arctic. Based on the significant amounts of processed federal statistical monitoring data, the article analyzes the main workforce training indicators in the vocational and higher education system of the federal subjects of the RF Arctic zone, including the number of students and the distribution of the students by fields of study, economic sectors, program forms, levels of education, sources of financing, as well as the dependence between the number of students and the population of the respective regions.

Statistical Monitoring of OSH: Analysis of Deviations and Recommendations for Optimization

Statistical Monitoring of OSH: Analysis of Deviations and Recommendations for Optimization

Discussion on Statistical Monitoring and Evaluation of High-Quality Development of County Economy

The county economy serves as the fundamental unit in China’s economic development and construction. There exists a significant correlation between the quality and level of county economic development and the overall economic construction level of the country. In recent years, China’s economy has experienced rapid growth, contributing to an improved living environment and substantial economic income for its citizens. However, this progress has also brought to light certain issues, such as an unbalanced industrial structure and inefficient resource utilization, leading to problems such as low efficiency and severe environmental pollution. Therefore, it becomes imperative to enhance the statistical monitoring and evaluation of high-quality county economic development. This approach aims to gather insights into the development status of the county economy and provide essential data support for decision-making in county economic development. Consequently, this paper proposes development suggestions for the statistical monitoring and evaluation of high-quality county economic development, serving as a crucial starting point for future initiatives.

Fault detection and diagnosis for chemical processes using dynamic global–local preserving projections

AbstractMost traditional multivariate statistical monitoring methods require an assumption that the observation values at a certain moment and a past moment are statistically independent. However, in actual chemical and biological processes, the sample at a certain moment is often affected by the previous moment. Therefore, given the problem of more false alarms and poor detection ability based on the traditional principal component analysis, this article proposes a dynamic global–local preserving projections (DGLPP) algorithm. Unlike dynamic local preserving projections (DLPP) and dynamic principal component analysis (DPCA), DGLPP controls the global and local information retained in the dimensionality reduction data by introducing weight coefficients, which makes the algorithm applicable to more types of industrial processes. Moreover, new parameter determination methods are also proposed for improved detection and diagnosis. Through the improved contribution graph method, we can see the influence degree of each variable on the fault, to monitor and isolate the fault. Finally, by verifying the operation of the multivariable process and two practical cases, the results show that compared with DPCA, DLPP, and global local retained projection (GLPP) methods, the performance under this method has been significantly improved.

Organizational aspects of the sanatorium-resort treatment of victims at work: Using the example of Kuzbass

BACKGROUND: The problem of studying the organizational aspects in the medical and social rehabilitation of people of working age who suffer from industrial injuries and occupational diseases requires special consideration, since in modern conditions, the sociopolitical and socioeconomic cost of restoring labor losses is associated with the persistent loss of ability to work among qualified workers. One of the priority areas in the medical and social rehabilitation of victims is sanatorium-resort treatment.
 AIMS: To evaluate the organization of providing sanatorium-resort treatment to victims at work.
 MATERIALS AND METHODS: Data were obtained from the official form of statistical observation of the activity of federal institutions of medical and social expertise № 7-sobes for 2022 in Kuzbass. The following methods were used: statistical analysis and content analysis of normative and methodological documents.
 RESULTS: According to the state form of statistical monitoring of the activities of federal institutions of medical and social examination № 7-social security for 2022 in Kuzbass, nearly all rehabilitation programs for victims contain medical rehabilitation measures — 24,452 (99.4%). Since the rehabilitation programs for victims include specific types, forms, and volumes of necessary rehabilitation measures that are optimal for the patients aimed at restoring and compensating for impaired body functions and restoring and compensating for the patient’s ability to carry out professional, household, and social activities, a typical list of services and measures of medical rehabilitation includes: medicines, 23,180 (94.3%); spa treatment, 21,127 (86.9%); and provision of medical products, 14,573 (61.8%). The most popular services for the medical rehabilitation of victims are sanatorium and resort services.
 CONCLUSIONS: The organizational algorithm for providing sanatorium-resort treatment to victims at work begins with the selection of organizations that provide sanatorium-resort treatment services. The selection is made by the regional branches of the fund on a competitive basis in accordance with the current legislation in the field of public procurement. The main organizational mechanism is implemented within the framework of interdepartmental interaction between three departments: healthcare (medical organizations), federal institutions of the medical and social expertise, and territorial departments of the Social Fund of Russia.

PSINDy: Probabilistic sparse identification of nonlinear dynamics for temporal process modeling and fault detection

BackgroundMultivariate statistical process monitoring (MSPM) methods have been widely studied and applied for dynamic process monitoring. However, limitations exist in the extant literature, as numerous extant MSPM approaches fail to concurrently account for the governing dynamics and inherent non-linearity of systems. MethodsTo handle the above-mentioned issue, a novel probabilistic dynamic process monitoring algorithm named probabilistic sparse identification of nonlinear dynamics (PSINDy) is proposed. In this algorithm, the Expectation–Maximization (EM) method is employed to estimate the parameters. In the E-step, the particle filtering technique is adopted to calculate corresponding latent expectations whose posterior distributions are not Gaussian. Furthermore, Hotelling's T2 and two novel monitoring statistics, termed Mahalanobis-based predictive error (MPE) and dynamic predictive error (DPE), are designed and utilized for fault detection. Significant FindingsThe effectiveness of the proposed method is validated through the Tennessee Eastman (TE) chemical process and the three-phase flow facility (TPFF).

Healthcare-associated infections in the constituents of Ural and Siberian Federal Districts: results of monitoring their identification and registration in 2022 and in long-term dynamics

Rationale. Monitoring of HAI (health care-associated infections) detection and registration of is one of the key components in the information subsystem of epidemiological surveillance for these infections. Despite a well-established HAI recording system in the Russian Federation as a whole, the constituents of the Ural and Siberian Federal Districts have different approaches to detection of these infections, which is confirmed by the final rating scores of their detection and registration.
 Objective. We studied HAI detection and registration in the constituents of the Ural and Siberian Federal Districts for ranking and rating their systems of epidemiological HAI surveillance.
 Materials and methods: The data of federal statistical monitoring forms No. 2 "Information on infectious and parasitic diseases" and No. 23-17 "Information on outbreaks of infectious diseases as well as from additional enquiry materials "Information on medical manipulations and cohorts treated" in 20182022 submitted by the Rospotrebnadzor offices of the respective Russian Federation constituents compared with the long-term and Russias nationwide averages. The quality of HAI registration in the healthcare institutions of the Ural and Siberian Federal Districts was rated using grouping by intensive incidence indicators, and the most significant groups of infections and risk cohorts were selected for analysis.
 Results: The results of the HAI detection and registration by the most significant groups of infections and risk cohorts among the patients and healthcare workers were analyzed. The data on the group and outbreak HAI morbidity in the healthcare institutions are presented. The quality of HAI detection and registration in the constituents of the Ural and Siberian Federal Districts was rated, and the subjects were ranked with regard to the activity of their epidemiological surveillance system.
 Conclusion: Active detection and registration of HAI cases over several years is typical for the healthcare institutions of Sverdlovsk and Chelyabinsk Oblasts as well as Yamalo-Nenets Autonomous District. Consistently low rating scores of the studied parameters are typical for the healthcare institutions of the Republics of Tyva, Altai and Krasnoyarsk Krai.