Statistical process control methods in the supervision and selective application of PID control

Radiation Portal Monitors (RPM) acquire sequential measurements of gamma activity that can be modeled as a sequence of Poisson distributed random numbers. These series can be analyzed with Statistical Process Control (SPC) methods to trigger alarms when the statistical distribution of the ambient dose during a transit changes. Some usual SPC methods have been compared and modified to narrow the time required to bound an alarm. Both the probabilities of detection and the Average Run Length (ARL) are used and fitted to curves to benchmark the SPC methods, which are ranked as: SR, EWMA, CUSUM, SPRT, FLR, and Shewhart.

Assessing the evidence from the use of SPC in monitoring, predicting & improving software quality

Statistical Process Control methods have proven effective in enhancing outcomes monitoring and improvement within various clinical domains. This study investigates their application in supporting thoracic surgical quality assurance processes across public hospitals in Queensland, Australia. Retrospective evaluation of clinical data from thoracic surgeries performed at five public hospitals between January 1, 2019, and June 30, 2023, was conducted. Performance benchmarks were developed and assessed. Exponentially Weighted Moving Average charts and Funnel Plots were utilised for analysis and reporting. To account for common cause variation, Risk Adjustment models were developed and incorporated into the monitoring process. Outcome indicators of service quality throughout patient care were examined. Clinical outcome indicators identified as being suitable quality markers included: Death within 90 days, Major Acute Morbidity, Return to Theatre, Length of Stay, Readmission within 30 days and Tumour/Node/Metastasis (TNM) Up/Down Staging. SPC tools revealed patterns of variation over time and between sites, prompting investigation into contributing factors and opportunities for quality improvement projects. While the primary goal is to efficiently and effectively identify instances of underperformance, a structured approach to signal investigation can be used to initiate quality improvement projects when sustained performance is identified that is better than expected. SPC tools effectively facilitate clinical performance monitoring, aiding in timely detection and intervention in instances of altered performance. Careful interpretation of performance charts assists in identifying both declines and positive experiences, fostering continuous learning and improvement.

Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part's surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q---Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.

Quality is a benchmark to determine the level of good and bad of a product. The level of quality of a product will affect customer satisfaction, hence, to produce high quality products, it is necessary for a company to have a quality control process. Quality control is a process that aims to maintain the quality of products and services that have been promised to consumers. In this study, quality control is carried out in the sugar packaging process. Where in the sugar packaging process found the resulting product has a poor quality, defective and not in accordance with specifications. This study uses the Statistical Process Control (SPC) method which aims to determine the causes of defective products with the intention that the packaging process can further minimize the level of product defects. The SPC method is a statistical analysis technique with seven statistical tools or seven tools. Based on the results of the study it can be seen that the cause of product damage / defects in the product packaging process is caused by three types of damage namely damage due to conveyor (38.17%), damage due to machine pinched (35.82%), and damage due to loose seams (26,00%) This type of damage can be caused by human error and other factors such as engine condition, engine cleanliness and the monitoring process. Proposed improvements recommended for the company are to provide training to employees, make clear work instructions, conduct periodic maintenance for the machines used, supervise all work areas, and carry out quality control for every acceptance of raw materials.

Biodiesel is a promising alternative fuel that can be obtained from vegetable oils or animal fats through transesterification reactions with alcohol. Biodiesel has numerous advantages compared to diesel oil, namely: it is a renewable energy resource, it is not toxic, and it is environmentally friendly because the raw material does not contain sulfur and low emissions. Biodiesel production is inseparable from the availability of raw materials until now the raw materials that meet the needs of production capacity are palm oil (CPO). To overcome this problem, in this study we want to find out how much biodiesel conversion percentage if we use other raw materials such as: CPO oil, corn oil, VCO oil, and waste cooking oil. The analysis used in this study was the analysis of the use of raw materials to the value of biodiesel conversion using the Statistical Process Control (SPC) method. SPC method was used to analyze, manage, control, and improve a product and process using statistics. The objective of this study was to produce high conversion percentage biodiesel and analyze and control the quality of research products. By having this statistical method, it could be found errors in a study or out of control production so that further action can be taken to overcome them. In this study, it was found that the product which was outside of the control limit was biodiesel made from CPO and waste cooking oil. The results of the analysis using cause and effect diagrams could determine the causes of damage in the production process, which come from the factors of workers/humans, production machines, working methods, materials/raw materials and work environment.

Six sigma process improvement in the emergency department: A pilot study using statistical process control methods to analyze ecg times in the emergency department

Statistical process control is the application of statistical methods to the measurement and analysis of variation process. Various regulatory authorities such as Validation Guidance for Industry (2011), International Conference on Harmonisation ICH Q10 (2009), the Health Canada guidelines (2009), Health Science Authority, Singapore: Guidance for Product Quality Review (2008), and International Organization for Standardization ISO-9000:2005 provide regulatory support for the application of statistical process control for better process control and understanding. In this study risk assessments, normal probability distributions, control charts, and capability charts are employed for selection of critical quality attributes, determination of normal probability distribution, statistical stability, and capability of production processes, respectively. The objective of this study is to determine tablet production process quality in the form of sigma process capability. By interpreting data and graph trends, forecasting of critical quality attributes, sigma process capability, and stability of process were studied. The overall study contributes to an assessment of process at the sigma level with respect to out-of-specification attributes produced. Finally, the study will point to an area where the application of quality improvement and quality risk assessment principles for achievement of six sigma-capable processes is possible. Statistical process control is the most advantageous tool for determination of the quality of any production process. This tool is new for the pharmaceutical tablet production process. In the case of pharmaceutical tablet production processes, the quality control parameters act as quality assessment parameters. Application of risk assessment provides selection of critical quality attributes among quality control parameters. Sequential application of normality distributions, control charts, and capability analyses provides a valid statistical process control study on process. Interpretation of such a study provides information about stability, process variability, changing of trends, and quantification of process ability against defective production. Comparative evaluation of critical quality attributes by Pareto charts provides the least capable and most variable process that is liable for improvement. Statistical process control thus proves to be an important tool for six sigma-capable process development and continuous quality improvement.

Monitoring and analyzing patients’ pathways by the application of Process Mining, SPC, and I-RTLS

Moving towards process-based radiotherapy quality assurance using statistical process control

Several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time. Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.

Questions about the validity of measurements made in an E/sup 3/ laboratory using software to control the instrumentation are a continuing concern for EMC engineers. In previous work, it has been shown that the use of statistical process control (SPC) methods to gage and track the variability of system measurements can provide confidence in the repeatability of the system hardware setup, and in the data it produces. Once the hardware system stability has been demonstrated, it is possible to also utilize SPC historical information to assist in confirming the validity of the data gathered using the system's control software.

<h3>Background</h3> Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal...

Additive Manufacturing (AM) technology is widely used. There is an increasing demand for quality of AM product. Currently, there is no ideal method to monitor and ensure the quality of AM during the modeling process. In AM, the model is built layer by layer and the defects emerge gradually, so the defects might not be found immediately during the manufacturing process. With the accumulation of the inaccuracies, the quality of model would be decreased. To promote the quality level of AM, quality control technology is essential. In this paper, the quality of Fused Deposition Modeling (FDM) is studied by the method of combining the Machine Vision (MV) and Statistical Process Control (SPC). Firstly, the picture of each layer during the manufacturing process is caught with a camera. Secondly, the contour of these pictures are extracted with image processing methods. Finally, these data is analyzed with the methods of statistical process control and the control status is acquired. In this paper, three different models are designed to verify the feasibility of this method. It can be concluded that all defects in counter profile can be detected and the monitoring accuracy is 0.5mm.

Sociotechnical systems analysis was employed to study the inputs, processes and outputs of a Navy warehouse complex. The analysis identified the key points that required control to maintain and improve quality and productivity. These points (key variances) are the most meaningful focus for measurement and analysis using the principles and methods of statistical process control (SPC). Existing data from the warehouse were analyzed using several SPC techniques (run charts and control charts) and presented to managers. This information provided them new insights into system capabilities and performance.