Statistical process monitoring approach for high-density point clouds

The paper describes the application of statistical process control (SPC) methods to supervise the control of a noisy process. SPC charts form a supervisory unit that monitors process outputs and selectively applies terms of a conventional PID controller. The advantages of this method lie in the ability of the control loop to distinguish between process noise and genuine error signals. This stops the controller reacting unnecessarily reducing actuator work. Results are presented for the control of a steam/water heat exchanger rig and show the effectiveness of this supervisory unit to produce quieter control without creating a lag in the control loop. >

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.

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

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.

<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.

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.

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.

Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection. We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated. Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44. CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively. CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Statistical process control (SPC) can help manufacturers improve the quality of their products. This paper describes a relatively simple and effective method and algorithm to adjust the accuracy of parts produced on CNC machine tools using the methods of statistical process control. Examined the possibilities of the proposed method by computer simulation of the process. By using the proposed algorithm scattering of size is placed in the middle of the tolerance zone. Various variants carrying out the method are proposed adequate on the machining time during processing details in the sample. This can optimize the total time for processing of the batch.

Manufacturing and service processes today usually involve more than one process stages and operations. With an emphasis on achieving satisfactory product and service quality, multistage processes surveillance and fault diagnosis has become a necessity. Statistical Process Control (SPC) methods have been widely recognised as effective approaches for process monitoring and diagnosis. However, most conventional SPC methods focus on single-stage process without considering the multistage scenario. In this paper, we attempt to offer comprehensive references of statistical control methods for multistage manufacturing and service operations. Existing methods are compared and some future research topics are discussed.

As suggested by Box and Kramer [1] and others, it is possible to reduce both the special cause and common cause variations by applying Statistical Process Control (SPC) methods to monitor the output of an Automatic Process Control (APC) controlled process. In this paper, we develop an economic model for SPC monitoring of APC controlled processes. We also develop an economic loss-based criterion, the Average Quality Cost (AQC), to evaluate the performance of SPC charting methods. The AQC and the traditional average run length of three common SPC charts are investigated and compared.