Statistical Process Control Methods Research Articles

Quality 4.0: Learning quality control, the evolution of SQC/SPC

This article marks a significant advancement in the field of quality management, specifically focusing on the evolution from traditional Statistical Quality Control (SQC) and Statistical Process Control (SPC) methods to a more advanced Learning Quality Control (LQC) approach. The research introduces Quality 4.0 (Q4.0) as a novel paradigm that fuses the technologies of the fourth industrial revolution, Manufacturing Big Data (MBD), Industrial Internet of Things (IIoT), Cloud Storage and Computing (CSC) and Artificial Intelligence (AI), with traditional quality management practices. The central theme of this study is exploring the limitations inherent in conventional quality control methods when faced with the complexities of modern manufacturing environments. The authors propose LQC systems as a solution, employing binary classification algorithms to predict and detect defects in manufacturing processes. This represents a shift from reactive to proactive quality measures enabled by AI’s real-time data processing capabilities. The document delves into the evolution of manufacturing data across industrial revolutions, highlighting the exponential growth of unstructured data and its challenges. Through case studies, the authors illustrate the practical applications of LQC systems, demonstrating their ability to learn complex patterns in hyperdimensional spaces and automate tasks traditionally performed visually.

Online Nonparametric Process Monitoring for IoT Systems Using Edge Computing

Facilitated by modern sensing and integrated circuit technology, Internet of Things (IoT) systems now have the ability to intelligently and automatically collect data, perform computational tasks on edge, and efficiently make decisions locally or remotely. However, existing multivariate statistical process control (MSPC) methods commonly assume that sensors directly transmit the raw measurements to the central server, and thus the edge computing capability of the IoT systems is not effectively harnessed. To fill this literature gap, we propose a monitoring scheme tailored for IoT systems by leveraging their edge computational power. In particular, inspired by the inherent hierarchical structure of the IoT system, we construct the monitoring statistics hierarchically. At each edge device, we first construct a nonparametric stream-level statistic for each data stream and aggregate them to derive the device-level statistics. To eliminate the inconsistency in device-level statistics across different edge devices, the device-level statistics are transformed into time-related statistics, which are then sent to the central server. At the central server, a system-level monitoring statistic is constructed, and the most informative edge devices that should transmit statistics are intelligently determined. Theoretical analyses are carried out to justify the effectiveness of the data transmission strategy. Both simulations and a case study, which is based on an IoT system we built, are conducted to evaluate the performance and validate the superiority of the proposed monitoring scheme.

Analisis Pengendalian Kualitas Produk Part Housing SUV Menggunakan Metode Statistical Process Control di PT. Y

In the increasingly advanced world of manufacturing industries, of course, companies need to innovate for tighter competition. The role of product quality will be very influential to get product results with good quality and in accordance with standard operating procedures, must carry out quality control by paying attention to the level of product defects in order to approach zero defects. Therefore, this study aims to analyze quality control to reduce the occurrence of defective products and provide improvement proposals. The method used is Statistical Process Control (SPC) to analyze product defects with the total production of SUV Housing Parts as many as 34740 units. 4 types of defects were produced, namely Coak Blank 40 units, Overlap 37 units, Ngecap Scrap 13 units, and Flatness NG 11 units. The factors causing this type of disability are human factors, machines, materials, environment, and methods. Thus, the improvement proposals given are providing regular training to operators, carrying out routine machine maintenance, using ear plugs while working, measuring material dimensions, and checking materials that will enter the machine. Based on the research conducted, this SPC method can help companies identify product defects and provide suggestions or solutions to problems that occur. Keywords: Defect, Quality Control, Repair, Statistical Process Control

Use of urine neutrophil gelatinase-associated lipocalin for nephrotoxic medication acute kidney injury screening in neonates.

Daily serum creatinine monitoring protocols for acute kidney injury (AKI) are invasive and may lead to surveillance resistance. We aimed to understand if use of urine neutrophil gelatinase-associated lipocalin (uNGAL) could increase high-risk nephrotoxic medication (NTMx) associated AKI screening adherence in neonates. Statistical process control methods prior to and post implementation were trended. The primary outcome, screening adherence, was defined as either daily serum creatinine or uNGAL assessment through 2 days post high-risk NTMx exposure. 1291 monitoring days from the pre-implementation era (4/2020-6/2021) were compared to1377 monitoring days from the post-era (6/2021-10/2022). AKI screening adherence increased (81 to 92%) following implementation of optional uNGAL screening. Urine NGAL accounted for 35% of screening obtained. Use of uNGAL resulted in a 40% reduction in blood sampling for serum creatinine. Incorporation of uNGAL as a complementary screening tool to serum creatinine demonstrated sustained increased AKI surveillance in our Baby NINJA monitoring program.

Improved Fault Detection Method based on HSMM

This paper proposes a fault detection method for multivariate statistical process control. The proposed method combines the Forward-Backward Hidden Semi-Markov Model (HSMM) and Principal Component Analysis (PCA). A stochastic automaton was used for multi-mode detection with many observation sequences. We used agglomerative clusters to find the initial parameters of HSMM. We allocated an adaptive threshold and a fixed threshold in each mode for fault detection with PCA, including Hotelling T2 statistic and squared predictive error (Q statistic). We simulated this method on the Tennessee Eastman Process (TEP). Some faults were designed with various runs and times of occurrence. The experimental results were compared with the Mixture Bayesian PCA, Hidden Markov Model (HMM), and HSMM methods. The results are robust with an efficient detection rate. This activity recommends ways to find action plans for multi-mode process monitoring in chemical plants.

ANALISIS PENGENDALIAN KUALITAS PRODUK MIE DENGAN METODE STATISTICAL PROCESS CONTROL

CV Manunggal Jaya is a company engaged in food production in Bandung. The company, which produces noodles experiences product defects that exceed the company's tolerance limit. The purpose of this study is to determine quality control, factors that cause product defects, and the application of the Statistical Process Control (SPC) method. This research method is a descriptive quantitative method with data collection using interviews, observation and documentation. Data processing is done using tools such as flowchart, fishbone diagram, check sheet, histogram, control chart (p-chart) and pareto diagram. Based on the research results that there are 3 (three) quality controls, namely: Quality control of raw materials, production processes, finished products. Product defects are caused by human factors, raw materials, machines, and methods. There are three types of defects that occur at CV Manunggal Jaya Bandung, namely size defects, texture defects and dirty dough. Based on the results of the control chart (p-chart), it is known that the CL is 0.0081, UCL is 0.00961, LCL is 0.00668. The results of the analysis using the Pareto diagram tool state that the priority of improvements that must be made by the company is on texture defects.

Online nonparametric monitoring for asynchronous processes with serial correlation

Existing multivariate statistical process control methods commonly require all data streams have the same sampling interval. In practice, this assumption may not be valid, as different sensors can have different sampling intervals. In this article, we first propose a generic nonparametric monitoring scheme to online monitor the asynchronous data streams without considering serial correlation. Then the proposed scheme is extended such that it can handle serially correlated data streams. Specifically, we construct a nonparametric local statistic for each data stream, which is sensitive to mean shifts. To eliminate the influence of different sampling intervals, our innovative idea is to transform the local statistics into time-related statistics according to the sampling intervals. A global monitoring scheme is then constructed based on the sum of top- r time-related statistics. To extend the proposed method for serially correlated data streams, we further propose a novel estimation method for the pairwise covariance functions and the data streams can be decorrelated accordingly. Numerical simulations and a case study are conducted, showing the effectiveness of the proposed method in handling asynchronous data streams with serial correlation.

Proposing an innovative real-time monitoring approach for the parameter stability of credit-default prediction model

In real-world applications, the default prediction process generally requires periodic recalibration of parameter estimates with up-to-date data. However, few studies have investigated sudden changes or unknown structural breaks in the regularly estimated parameters, which could lead to biases in the models and thus affect the prediction accuracy. The instability of estimated parameters has been shown to under- or overestimate prediction results, but their real-time monitoring remains unexplored. This paper aims to fill this research gap by monitoring the estimated parameter stability in default prediction models in real time using statistical process control methods. We innovatively transform this issue into a research problem of profile monitoring and examine the monitoring performance of three control charts using Monte Carlo simulation. Simulation experiments demonstrate that the self-starting multivariate exponentially weighted moving average control chart outperforms other charts in detecting speed and range. It can also predict credit crises, detect more minor changes in risk, and describe their changing trends in real time. Throughout this article, we employ a forward intensity approach, which has been put into long-term applications for corporate default predictions, to illustrate the profile monitoring process.

Measuring the quality of the learning process in basic courses using the multivariate statistical process control method

The quality of learning is the key to being able to compete in the world of education. This research aims to measure the quality of the learning process in basic courses at the University of Logistics and International Business. The method used is Multivariate Statistical Control Process with subgroups whose size is not constant. In this method, multivariate control of the target process is carried out using the T2 Hotelling control diagram. Meanwhile, control of multivariate process variability is carried out using a generalized variance control chart. The sample data used in this research was taken from grades in English courses and introductory logistics courses from 20 classes with a total of 498 students. Data collection was carried out randomly from 2019-2022. The results obtained were that in the Multivariate Process Target (MPT) calculation using the T2 Hotelling control diagram it was found that class 1 was out of control. Meanwhile, in calculating Multivariate Process Variability (MPV) using a Generalized Variance (GV) based control diagram, it was found that classes 5 and 20 were out of control. Investigating the cause of the class being out of control is a part that must be explored by study program and university leaders in an effort to improve the teaching and learning process.

Detecting mean changes in experience sampling data in real time: A comparison of univariate and multivariate statistical process control methods.

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T², EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T² procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Coming in Hot: A quality improvement approach to improving care of febrile infants

Abstract Background and Objectives Significant practice variation exists in managing young infants with fever. Quality improvement strategies can aid in risk stratification and standardization of best care practices, along with a reduction of unnecessary interventions. The aim of this initiative was to safely reduce unnecessary admissions, antibiotics, and lumbar punctures (LPs) by 10% in low-risk, febrile infants aged 29 to 90 days presenting to the emergency department (ED) over a 12-month period. Methods Using the Model for Improvement, a multidisciplinary team developed a multipronged intervention: an updated clinical decision tool (CDT), procalcitonin (PCT) adoption, education, a feedback tool, and best practice advisory (BPA) banner. Outcome measures included the proportion of low-risk infants that were admitted, received antibiotics, and had LPs. Process measures were adherence to the CDT and percenta­ge of PCT ordered. Missed bacterial infections and return visits were balancing measures. The analysis was completed using descriptive statistics and statistical process control methods. Results Five hundred and sixteen patients less than 90 days of age were included in the study, with 403 patients in the 29- to 90-day old subset of primary interest. In the low-risk group, a reduction in hospital admissions from a mean of 24.1% to 12.0% and a reduction in antibiotics from a mean of 15.2% to 1.3% was achieved. The mean proportion of LPs performed decreased in the intervention period from 7.5% to 1.8%, but special cause variation was not detected. Adherence to the CDT increased from 70.4% to 90.9% and PCT was ordered in 92.3% of cases. The proportion of missed bacterial infections was 0.3% at baseline and 0.5% in the intervention period while return visits were 6.7% at baseline and 5.0% in the intervention period. Conclusions The implementation of a quality improvement strategy, including an updated evidence-based CDT for young infant fever incorporating PCT, safely reduced unnecessary care in low-risk, febrile infants aged 29 to 90 days in the ED. Purpose To develop and implement a multipronged improvement strategy including an evidence-based CDT utilizing PCT to maximize value of care delivered to well-appearing, febrile infants presenting to EDs.

Reducing Unplanned Intubations in the Neonatal Intensive Care Unit After Children's Surgery: A Quality Improvement Project

BackgroundUnplanned intubation following children's surgery is associated with increased postoperative mortality. In response to being a National Surgical Quality Improvement Program - Pediatric (NSQIP-P) high outlier for postoperative unplanned intubation, we aimed to reduce postoperative unplanned intubation events by 25% in one year. Methods/InterventionA multidisciplinary team of stakeholders was assembled in 2018. Most unplanned intubation events occurred in the neonatal intensive care unit (NICU). Based on apparent causes of unplanned intubations identified in case reviews, an extubation readiness checklist and a postoperative pain management guideline emphasizing non-opioid analgesics were implemented for NICU patients in September 2019. Postoperative unplanned intubation events were tracked prospectively and evaluated using quality improvement statistical process control methods. ResultsUnplanned intubations in the NICU decreased from 0.27 to 0.07 events per patient in the post-intervention group (September 2019–June 2022, n = 145) compared to the pre-intervention group (January 2016–August 2019, n = 200), representing a 76% reduction. Postoperative opioid administration decreased significantly, while acetaminophen usage increased significantly over time. Balancing measures of postoperative pneumonia rate (1.5% vs 0.0%, p = 0.267) and median hospital length of stay [40 (IQR 51) days vs 27 (IQR 60), p = 0.124] were not different between cohorts. The 30-day mortality rate for postoperative patients in the NICU significantly declined [6.5% (n = 13) vs 0.7% (n = 1), p < 0.001]. ConclusionsPostoperative unplanned intubation rates for NICU patients decreased following a quality improvement effort focused on opioid stewardship and extubation readiness. Type of StudyProspective Quality Improvement. Level of EvidenceLevel III.

Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.

Tolerance limit is defined on pre-treatment patient specific quality assurance results to identify "out of the norm" dose discrepancy in plan. An out-of-tolerance plan during measurement can often cause treatment delays especially if replanning is required. In this study, we aim to develop an outlier detection model to identify out-of-tolerance plan early during treatment planning phase to mitigate the above-mentioned risks. Patient-specific quality assurance results with portal dosimetry for stereotactic body radiotherapy measured between January 2020 and December 2021 were used in this study. Data were divided into thorax and pelvis sites and gamma passing rates were recorded using 2%/2mm, 2%/1mm, and 1%/1mm gamma criteria. Statistical process control method was used to determine six different site and criterion-specific tolerance and action limits. Using only the inliers identified with our determined tolerance limits, we trained three different outlier detection models using the plan complexity metrics extracted from each treatment field-robust covariance, isolation forest, and one class support vector machine. The hyperparameters were optimized using the F1-score calculated from both the inliers and validation outliers' data. 308 pelvis and 200 thorax fields were used in this study. The tolerance (action) limits for 2%/2mm, 2%/1mm, and 1%/1mm gamma criteria in the pelvis site are 99.1% (98.1%), 95.8% (91.1%), and 91.7% (86.1%), respectively. The tolerance (action) limits in the thorax site are 99.0% (98.7%), 97.0% (96.2%), and 91.5% (87.2%). One class support vector machine performs the best among all the algorithms. The best performing model in the thorax (pelvis) site achieves a precision of 0.56 (0.54), recall of 1.0 (1.0), and F1-score of 0.72 (0.70) when using the 2%/2mm (2%/1mm) criterion. The model will help the planner to identify an out-of-tolerance plan early so that they can refine the plan further during the planning stage without risking late discovery during measurement.

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.

Development of Statistical Process Control Methods for Anisotropic Magnetoresistive Sensor Technology

An application of some statistical process control methods for production process of thin anisotropic ferromagnetic films are described. There was calculated the correlation between the thin film deposition rate, substrate's temperature, time of deposition and surface resistivity, magnetoresistive effect, anisotropy field, coercive force of thin film. It was shown that more strong correlation (rk = 0,78) with AMR-effect's value in film observe for surface resistivity that can use for AMR-effect rising.

Registration-Free Localization of Defects in Three-Dimensional Parts from Mesh Metrology Data Using Functional Maps

We consider a common problem occurring after using a statistical process control (SPC) method based on three-dimensional measurements: locate where on the surface of the part that triggered an out-of-control alarm there is a significant shape difference with respect to either an in-control part or its nominal (computer-aided design (CAD)) design. In the past, only registration-based solutions existed for this problem, which first orient and locate the part and its nominal design under the same frame of reference. Recently, spectral Laplacian methods have been proposed for the SPC of discrete parts and their measured surface meshes. These techniques provide an intrinsic solution to the SPC problem: that is, a solution exclusively based on data whose coordinates lie on the surfaces without making reference to their ambient space, thus avoiding registration. Registration-free methods avoid the computationally expensive, nonconvex registration step needed to align the parts as required by previous methods, eliminating registration errors, and they are important in industry because of the increasing use of portable noncontact scanners. In this paper, we first present a new registration-free solution to the post-SPC part defect localization problem. The approach uses a spectral decomposition of the Laplace–Beltrami operator in order to construct a functional map between the CAD and measured manifolds to locate defects on the suspected part. A computational complexity analysis demonstrates the approach scales better with the mesh size and is more stable than a registration-based approach. To reduce computational expense, a new mesh partitioning algorithm is presented to find a region of interest on the surface of the part where defects are more likely to exist. The functional map method involves a large number of point-to-point comparisons based on noisy measurements, and a new statistical thresholding method used to filter the false positives in the underlying massive multiple comparisons problem is also provided. Funding: This research was partially funded by the National Science Foundation [Grant CMMI 2121625]. Data Ethics &amp; Reproducibility Note: There are no data ethics considerations. The code capsule is available on Code Ocean at https://codeocean.com/capsule/4615101/tree/v1 and in the e-Companion to this article (available https://doi.org/10.1287/ijds.2023.0030 ).

Predictive quality assurance for linear accelerator target failure using statistical process control

The performance of a linear accelerator (Linac) depends on the integrity of its x-ray target. The sudden failure of its target not only breaks down the Linac but also could contribute significant disruptions to patient care. This work is to develop a predicative quality assurance (QA) method using Statistical Process Control (SPC) and AutoRegressive Integrated Moving Average (ARIMA) modeling to identify the risk of target failure before it occurs. In the past years, we observed two incidents of target failure among our Linacs. Retrospectively, we collected past daily QA data (from both open fields and enhanced dynamic wedge (EDW) measurements) and analyzed its historical trend using methods of SPC and ARIMA. SPC is a technique that monitors process performance based on statistical analysis. ARIMA is a time-series forecasting algorithm that can be used to estimate future values based on its past pattern. Both have been evaluated for predictive QA in radiotherapy. Application of SPC on open beam QA data would not yield an early warning signal to the pending target failures. However, when the same SPC methodology applies to EDW measurements, the control limits were breached a couple of weeks before the target failed. EDW mechanism introduces nonuniform magnification factors over its wedge-directed beam profiles and is responsible for the sensitivity of its profile to changing beam properties induced by a degrading target. Further extension of the warning period may be possible by using ARIMA modeling. Predicative QA for EDW daily data using SPC and ARIMA methods may provide an early QA warning to incoming Linac target failure.

Analysis of Quality Defects in Plastic Seed Products Using Statistical Process Control (SPC) Method

Plastic seed products are products made from recycled plastic materials such as raw materials for plastic bags and some screen printing plastics. UD. ABC is a company that produces PP (Polypropylene) plastic pellets. The existing problems include the discovery of instability in the production of plastic pellets which is in a negative direction for the company, namely there are product defects in plastic pellet products that exceed the standard defect tolerance limit of the company by 2%. Based on the Pareto diagram, it can show that the type of protrusion defect is 69.56% more dominant than the type of connecting defect, which is 30.44%. Based on the control chart based on the p-chart, it shows that some dates are outside the limits of the control chart, namely the sample lot number 3 (March) of 0.0352, 4 (April) 0.0273, 7 (July) 0, 0134, 8 (August) 0.271. The data that comes out of the control chart limits exceeds the upper control limit (BKA/UCL) of 0.02649 and the lower control limit (BKB/LCL) of 0.02395. So what must be done to repair the engine factor is to carry out periodic briefings on how the machine works starting from setting the temperature and changing the filter. Meanwhile, the human factors section, namely directing the replacement of machine filter workers with temperature operators so that workers do not get tired easily, conducting periodic supervision of each worker for the leader and providing guidance on knowledge about machine work systems, mixing raw materials and operating machine temperature controls regularly.

Data Mining Technology and Its Applications in Coal and Gas Outburst Prediction

Coal and gas outburst accidents seriously threaten mine production safety. To further improve the scientific accuracy of coal and gas outburst risk prediction, a system software (V1.2.0) was developed based on the C/S architecture, Visual Basic development language, and SQL Server 2000 database. The statistical process control (SPC) method and logistic regression analyses were used to assess and develop the critical value of outburst risk for a single index, such as the S value of drill cuttings and the K1 value of the desorption index. A multivariate information coupling analysis was performed to explore the interrelation of the outburst warning, and the prediction equation of the outburst risk was obtained on this basis. Finally, the SPC and logistic regression analysis methods were used for typical mines. The results showed that the SPC method accurately determined the sensitivity value of a single index for each borehole depth, and the accuracy of the logistic regression method was 94.7%. These methods are therefore useful for the timely detection of outburst hazards during the mining process.

Frozen Food Quality Control Using Statistical Process Control Methods and Failure Mode and Effect Analysis

Frozen Food Quality Control Using Statistical Process Control Methods and Failure Mode and Effect Analysis