Statistics Decomposition and Monitoring in Original Variable Space

The traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.

Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart. The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

Research Civil Aircraft Model (RCAM) Dengan Metode Dinamis 6 Derajat Kebebasan Menggunakan MATLAB

Model Pesawat merupakan suatu usaha untuk menciptakan atau membuat simulasi pesawat yang sebenarnya dan model ini bisa disebut Research Civil Aircraft Model (RCAM), yaitu model pesawat sipil bermesin ganda yang dikembangkan oleh Group for Aeronautical Riset dan Teknologi di Eropa (GARTEUR). Model pesawat ini mirip dengan Boeing 757-200. Research Civil Aircraft Model yang dibuat menggunakan 6 derajat kebebasan. 6 derajat kebebasan terdiri dari 3 translasi yaitu 3 derajat untuk koordinat kartesian pada sumbu (x,y,z) dan 3 rotasi yaitu 3 derajat (pitch, roll, dan yaw) yang digunakan untuk mengontrol defleksi surface dan posisi throttle. Untuk membuat model, diperlukan parameter, yaitu parameter dari massa pesawat dan parameter dari chord aero dan juga CoG (Center of Gravity) dari suatu pesawat. Algoritma yang dimasukkan berasal dari control limits/Saturasi, Variabel Intermediate, Koefisien Force, Koefisien Momen, Efek Propulsi, Efek Gravitasi hingga akhirnya disimulasikan menggunakan “Simulink” pada MATLAB. Agar yang dihasilkan tidak hanya grafik maka diperlukannya animasi untuk melihat sikap pesawat sehingga digunakanlah 3D Animation pada MATLAB. Hasil dari permodelan pesawat sipil ini untuk melihat simulasi stabilitas dari aileron, rudder, elevator, dan throttle saat pesawat itu terbang. Hasil yang ada dapat berubah-ubah karena pada 3D Animation, pesawat dapat dikendalikan dari sikap pesawat saat miring kanan atau kiri dan juga saat pesawat berguling. Dengan adanya hal ini diharapkan simulasi tersebut bisa effective untuk melihat hasil yang sebenarnya saat pesawat terbang dan juga bisa dijadikan untuk simulator sebelum pesawat tersebut lepas landas.

Impact of Measurement Error on the Power Function of Average Control Chart under Non-Normal Population

In the present article, effect of measurement error on the power function of control charts for mean with control limits is considered based on non-normal population. The non-normality is represented by the first four terms of an Edge-worth series. Tabular and visual comparison is also provided for the better comprehension of the significance of measurement error on power function under non-normality.

Nutritional status of children living within institution-based care: a retrospective analysis with funnel plots and control charts for programme monitoring

ObjectivesThe aim of this study is to fill a key information gap on the nutrition-related epidemiology of orphaned and vulnerable children living within institution-based care (IBC) across six countries.DesignA retrospective analysis with Shewhart control charts and funnel plots to explore intersite and over time variations in nutritional status.SettingWe conducted a retrospective analysis of records from Holt International’s Child Nutrition Programme from 35 sites in six countries; Mongolia, India, Ethiopia, Vietnam, China and the Philippines.ParticipantsDeidentified health records from Holt International’s online nutrition screening database included records from 2926 children, 0–18 years old. Data were collected from 2013 to 2020 and included demographic and health information.ResultsAt initial screening, 717 (28.7%) children were anaemic, 788 (34.1%) underweight, 1048 (37.3%) stunted, 212 (12.6%) wasted, 135 (12%) overweight or obese and 339 (31%) had small head circumference. Many had underlying conditions: low birth weight, 514 (57.5%); prematurity, 294 (42.2%) and disabilities, 739 (25.3%). Children with disabilities had higher prevalence of malnutrition compared with counterparts without disabilities at baseline and 1-year screenings. There was marked intersite variation. Funnel plots highlight sites with malnutrition prevalence outside expected limits for this specific population taking into consideration natural variation at baseline and at 1 year. Control charts show changes in site mean z-scores over time in relation to site control limits.ConclusionsMalnutrition is prevalent among children living within IBC, notably different forms of undernutrition (stunting, underweight, wasting). Underlying risk factors are also common: prematurity, low birth weight and disability. Nutrition interventions should take into account the needs of this vulnerable population, especially for infants and those with disabilities. Using control charts to present data could be especially useful to programme managers as sites outside control limits could represent: problems to be investigated; good practices to be shared.

Comparison of Some Non-Parametric Quality Control Methods

Multivariate Non-Parametric control charts were used to monitoring the data that generated by using the simulation, whether they are within control limits or not. Since that non-parametric methods do not require any assumptions about the distribution of the data. This research aims to apply the multivariate non-parametric quality control methods, which are Multivariate Wilcoxon Signed-Rank ( ) , kernel principal component analysis (KPCA) and k-nearest neighbor ( −

Funnel Plots for Assessing Institutional Performance

<p>This thesis presents a comparison of maternal outcomes for births in New Zealand District Health Boards (DHBs).This is carried out through analysis of the National Minimum Dataset collected by the Ministry of Health for 2007. The outcome compared is postpartum haemorrhage (PPH) the results are displayed using funnel plots, a useful tool for displaying unbiased information on performance outcomes when comparing institutions. Exploration of the data found that there are differences in the demographics, maternal and birth characteristics among DHBs. The rates of PPH are different and the population mixes are made up of a range of different proportions of ethnic groups, ages and deprivation indexes. The exploratory analysis found that a large number of factors are associated with PPH. And that birth weight, parity and gestation had a large number of missing observations. These factors are not missing at random and require imputing prior to constructing the funnel plots. Results show that there is divergence amongst DHBs in the postpartum haemorrhage rate. First a raw PPH rate was plotted and the results indicated there were differences among DHBs. As there are many potential predictors for PPHa logistic regression model was applied to find the most important factors related to PPH. This allows us to apply an adjusted rate for the funnel plot. The risk adjusted funnel plot also indicated differences among DHBs. Two approaches are taken to account for the overdispersion. A winsorised estimate and a winsorised estimate with a random effects term are applied to the data. The approaches produced different results. The winsorised estimate widened the control limits and the random effects term narrowed the control limits. All four plots identified an extreme outlier and this was later removed from the analysis and the winsorisation funnel plots were rerun. The influential outlier made a difference and from this we can concluded that 2 out 20 DHBs lie outside the 95% control limits. These two DHBs could be stated as having a very low rate of PPH.</p>

Funnel Plots for Assessing Institutional Performance

<p>This thesis presents a comparison of maternal outcomes for births in New Zealand District Health Boards (DHBs).This is carried out through analysis of the National Minimum Dataset collected by the Ministry of Health for 2007. The outcome compared is postpartum haemorrhage (PPH) the results are displayed using funnel plots, a useful tool for displaying unbiased information on performance outcomes when comparing institutions. Exploration of the data found that there are differences in the demographics, maternal and birth characteristics among DHBs. The rates of PPH are different and the population mixes are made up of a range of different proportions of ethnic groups, ages and deprivation indexes. The exploratory analysis found that a large number of factors are associated with PPH. And that birth weight, parity and gestation had a large number of missing observations. These factors are not missing at random and require imputing prior to constructing the funnel plots. Results show that there is divergence amongst DHBs in the postpartum haemorrhage rate. First a raw PPH rate was plotted and the results indicated there were differences among DHBs. As there are many potential predictors for PPHa logistic regression model was applied to find the most important factors related to PPH. This allows us to apply an adjusted rate for the funnel plot. The risk adjusted funnel plot also indicated differences among DHBs. Two approaches are taken to account for the overdispersion. A winsorised estimate and a winsorised estimate with a random effects term are applied to the data. The approaches produced different results. The winsorised estimate widened the control limits and the random effects term narrowed the control limits. All four plots identified an extreme outlier and this was later removed from the analysis and the winsorisation funnel plots were rerun. The influential outlier made a difference and from this we can concluded that 2 out 20 DHBs lie outside the 95% control limits. These two DHBs could be stated as having a very low rate of PPH.</p>

135. Influence of Prescribers on Antibiotic Use among Skilled Nursing Care Residents in 29 U.S. Nursing Homes

Background In nursing homes, federal mandates call for more judicious use of antibiotics and antipsychotics. Previous research indicates that practice patterns of nursing home practitioners, rather than resident’s signs and symptoms or overall medical conditions, drive antibiotic use. We hypothesized that nursing home practitioners who prescribe antibiotics more frequently than their peers may display a similar practice pattern for antipsychotics. Here, we examine similarities in prescribing patterns for antibiotics and antipsychotics among practitioners at 29 U.S. nursing homes. Methods Prescription data came from 2016 invoices from a pharmacy common to all 29 nursing homes. We defined practitioners as individuals who prescribed ≥1% of systemic medications at a nursing home and excluded practitioners without no prescriptions for anti-hypertensive drugs assuming they were not treating a general nursing home population (i.e. treating hospice or dementia patients). Using anti-hypertensive starts for standardization, we calculated the expected number of starts for both antibiotics and antipsychotics. Using funnel plots with Poisson 99% control limits for the observed-to-expected ratio, we identified practitioners whose use of either class of drugs exceeded these control limits. Practitioners were classified as high, average, or low prescribers for each class of drugs. Results We analyzed 129 practitioners who wrote for 113669 systemic medications. For antibiotics, 27 (20%) and 19 (15%) of practitioners were low and high prescribers, respectively. For antipsychotics, 53 (41%) and 14 (11%) were low and high prescribers, respectively (Figure 1). Among the low antibiotic prescribers, 59% (16/27) were also low antipsychotic prescribers. Among the high antibiotic prescribers, 21% (4/19) were also high antipsychotic prescribers (Figure 2). Figure 1. (a) Funnel plot for antibiotics (b) Funnel plot for antipsychotics Figure 2. Type of prescriber Conclusion Practitioners who were low prescribers for antibiotics were also likely to be low prescribers for antipsychotics, suggesting judicious use for both classes of medications. Further understanding of the behaviors of these individuals, as well as those who are high prescribers for both classes, has implications for improving antibiotic stewardship practices in nursing homes. Disclosures Robin L. Jump, MD, PhD, Pfizer (Individual(s) Involved: Self): Consultant