Statistical Process Control Technology Research Articles

Wind turbine condition monitoring based on double‐layer ensemble KNN method

AbstractAn ensemble K‐nearest neighbor model based on the double‐layer sampling method was proposed for the condition monitoring of wind turbine (WT) gearboxes. The distance function was improved to affinity distance, which helped overcome the limitation of obtaining local optimums. The feature priority was calculated based on the regularized mutual information, and a double‐layer sampling method combining data and feature sampling was designed. On the basis of the statistical process control technology, the warning threshold was designed and the real‐time residual was analyzed. The condition of the gearbox was monitored by combining the failure rate. Experimental results with real supervisory control and data acquisition (SCADA) data demonstrated that the proposed method improved the estimation accuracy of the model and could realize early fault warnings of a WT gearbox approximately 20 days earlier than the SCADA system.

Capacity Analysis of Cable Insulation Crosslinking Production Process Based on IoT Big Data

In the cable insulation crosslinking production process, the process quality is determined by multiple processes and multiple related process variables, and there are complex autocorrelation and cross-correlation among these process variables. In this paper, different process variables are studied. By implementing statistical process control on a univariate, single value control chart (I) and moving range control chart () are drawn, and process capability CP is analyzed to find variables with weak process capability for improvement. At the same time, based on the background of the continuous development of the Internet of Things and big data technology, this paper evaluates the complex autocorrelation and cross-correlation between the overall process variables, adopts the multivariate statistical process control technology, and combines the multivariate HOTELLING T2 control chart and MEWMA control chart to reflect the statistical characteristics of multiple variables, thus avoiding misjudgment or omission of process quality monitoring model. By studying the setting of cable production process parameters and quality standards, combined with the application of complete multivariate statistical process control (MSPC) around the evaluation results, process, and technical requirements, this research can provide effective guidance for the cable insulation crosslinking production process

Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

Simple SummaryThe management of body condition scores of dairy cows during the dry period affects postpartum health. It is therefore important to monitor the body condition scores so as to predict the occurrence of postpartum diseases. In this study, statistical process control technology was used to monitor body condition scores and to explore its effects on nutritional strategies and the development of postpartum subclinical ketosis. The results showed that the statistical process control technology was not a good tool for the diagnosis of postpartum subclinical ketosis, but an appropriate tool for the guidance of nutritional strategies.The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.

Application of statistical process control technology in operation optimization of thermal power units

The reliability of power plant data becomes more and more important with the development of smart power plant based on big data and artificial intelligence technology. Statistical process control technology is widely used in product production process management, but it has never been involved in thermal power. In this paper, the statistical process control technology is applied to the thermal power industry. The application results show that the statistical process control technology can not only take the calculation results as the quantitative criteria for the operation conditions of the unit, but also help to select the comprehensive optimal conditions of the unit under the specific conditions, and implement the goal of improving the management level and economic benefits of the power plant.

Quality control of linac in daily volumetric-modulated arc therapy

Objective To verify the daily delivery accuracy of volumetric-modulated arc therapy(VMAT) and achieve the process quality control (PQC) of linac using statistical process control (SPC)technology. Methods The log files of all treatments were taken out from the linac system. An in-house software which created by Matlab 7.14 was used to analyze the daily parameters accuracy and the mechanism and dose delivery accuracy of the linac for each VMAT, and an daily evaluation report was automatically created for the linac. The stability of the linac and the deviation of the delivery accuracy for various cancer sites were also analyzed. To achieve the process quality control of linac using SPC technology, the control limit of each parameter was calculated by Johnson alternation and the single value control charts were drawn. Results 76 patients mainly with nasopharyngeal carcinoma, cervical carcinoma, rectal cancer and laryngeal cancer, got the treatment daily with VMAT technology, and a total of 2 446 arcs were delivered by the linac in continuous 16 days. The gantry angle error, y and x collimator position error, MLC leaf position error and dose delivery error were 0.49°, 0.09 mm, 0.38 mm, 0.31 mm, 0.05 MU, respectively. Compare to the nasopharynx, uteri, rectal and larynx cancer, it can be seen that the variation factors of dose delivery and gantry angle errors for various cancer sites were higher, which was 8.10%and 4.54%, respectively. Through the process quality control of the linac, it was found that all the parameters were in control. While some abnormal points arose (the error was greater than UCL) , and the ratio of the gantry angle which was out of control (0.45%) was the highest in all parameters. Conclusions linac delivery accuracy could be monitored and verified during the whole VMAT treatment for each patient, and the daily running condition of linac could be monitored. Through the introduction of SPC technology and the control charts, the process quality control of linac in VMAT could be achieved. It is a financial and valid method for the daily quality assurance of linac. Key words: Volumetric-modulated arc therapy; linac; Statistical process control; Process quality control; Position accuracy

Group Technology Applied in Quality Management System

This paper is based on a company that manufactured the nuclear power components. This paper Studies the quality management that aimed at characteristics of multi specification and small batch production. The components which are produced at this company have been used in new energy field. Quality of nuclear power components can meet the technical requirements or not is very important to the operation and safeguard of nuclear power station in future. Group technology combined with statistical process control technology and applied in the quality management system to analyze the testing data of each working procedure in multi-kind and small-quantity production to achieve the goal of improve and control the quality of the product.

The Application Research of Statistical Process Control in Quality Management

By discussing the application of statistical process control technology in the print and package industry, we have been in control of the productive process, for focusing on the heat-seal intensity index of sauce package products, and utilized instability of the control chart discovering the instable in the process , to realize the pre-alarm function. The management makes the process update constantly, and enhances the process capability.

Application of Statistical Process Control Technology and Quality Tools in the Process of Series Motor Production

Application of Statistical Process Control Technology and Quality Tools in the Process of Series Motor Production

Statistical Process Control Technology Applied in ERP System

For the high and strict quality requirement in the manufacturing process of nuclear power parts, this paper is based on the combination of Statistical Process Control technology and the ERP quality management and control the production quality based on the control chart. PowerBuilder 9.0 and SQL Server2000 were used to design and develop the system while PowerBuilder 9.0 as front-end development tool and SQL Server2000 as back-end DBMS respectively. Firstly, collect the quality data of the production process (some important processes). Then, analysis these data and form control chart. Real-time monitor production process by the control charting to ensure the process is stability. Organic combination of SPC and ERP to improve and control the quality, not only enrich the analytical data of SPC, but also make up the ERP data to analysis and control quality data.

The Study and Application of Metal Injection Molding of Fe-Ni-Co Alloy

Packaging material of electronic goods requires higher strength, higher reliability, and lower expansion coefficient .What’s more, forming process is important. The Fe-Ni-Co alloy has a low expansion coefficient and good structure stability, so it is often used for packaging materials for the semiconductor industry. The shape of electronic packaging parts is so complex that the traditional manufacturing processes are difficult to form and it costs more, which greatly limits the application of Fe-Ni-Co alloy in complex electronic packaging parts. Metal (ceramic) powder injection molding (MIM / CIM) is a new near net shape technology. In this paper, we analyze the structures and characteristics of Gehause which is an electronic packaging box that has been successfully used in the MIM process to produce it. In this process, we adopted a new material of Fe-Ni-Co alloy, a specia binder and SPC (Statistical Process Control) technology which control parameters of injection molding and weight of parts. After sintering, the mechanical properties and precision of finished products meet all demands. Therefore, MIM technology can greatly improve the production efficiency, save materials and reduce production costs in producing Gehause.

An SPC monitoring system for cycle-based waveform signals using haar transform

Due to the rapid development of computer and sensing technology, many measurements of process variables are readily available in manufacturing processes. These measurements carry a large amount of information about process conditions. It is highly desirable to develop a process monitoring and diagnosis methodology that can utilize this information. In this paper, a statistical process control monitoring system is developed for a class of commonly available process measurements-cycle-based waveform signals. This system integrates the statistical process control technology and the Haar wavelet transform. With it, one can not only detect a process change, but also identify the location and estimate the magnitude of the process mean shift within the signal. A case study involving a stamping process demonstrates the effectiveness of the proposed methodology on the monitoring of the profile-type data. Note to Practitioners-Cycle-based signal refers to an analog or digital signal that is obtained through automatic sensing during each operation cycle of a manufacturing process. The cycle-based signal is very common in various manufacturing processes (e.g., forming force in stamping processes, the holding force, and the current signals in spot welding processes, the insertion force in the engine assembly process). In general, cycle-based signals contain rich process information. In this paper, cycle-based signal monitoring will be accomplished by monitoring the wavelet transformation of the signal, instead of monitoring the raw observations themselves. Further, a decision-making technique is developed using the SPC monitoring system to locate where the mean shift occurred and to estimate magnitudes of mean shifts. Thus, this paper presents a generic framework for the enhanced statistical process control technique of cycle-based signals.

OBM, SPC, and Theory D:

The problems confronting U.S. manufacturers whose products must compete with high quality products of foreign manufacturers are introduced. Major competitors are located in Japan where a competitive edge has been gained through applications of Statistical Process Control (SPC) technologies and innovative human resource management techniques. The concepts composing SPC are introduced and briefly compared to Skinner's (1938) approach to behavior analysis. SPC and behavior analysis involve a search for the cause(s) of variation in a system's outputs for purposes of output prediction and control. The 14 points which constitute Deming's (1982) Theory D are then presented. Theory D is a set of principles to guide managers interested in improving production quality using SPC. It deals with human resource management issues as well as SPC control technologies. The points are similar to OBM in that they provide prescriptions and proscriptions for managers interested in SPC technology while OBM includes advice to managers concerning implementation of technologies derived from behavior analyses. The article concludes with a brief discussion of the OBM research issues suggested by SPC as a systems control technology and Theory D as an approach to its implementation.