scholarly journals CONTRIBUTION TO THE SPC IMPLEMENTATION BY SELECTING AN APPROPRIATE VARIABLE CONTROL CHART

2021 ◽  
Vol 24 (1) ◽  
pp. 50-54
Author(s):  
Mite Tomov ◽  
◽  
Leonard Abazovski ◽  
Anastasija Ignjatovska ◽  
◽  
...  
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Mean Value ◽  
Practical Implementation ◽  
Statistical Process ◽  
Serial Production ◽  
Variable Control ◽  
Percentage Difference ◽  
The Mean ◽  
Distribution Type

The research in this paper contributes to the practical implementation of the Statistical Process Control (SPC), primarily in the serial production by selecting an appropriate variable control chart. The paper proposes a procedure algorithm where the deciding criteria used to select the variable control chart include: the data distribution type (whether an approximately normal distribution or not), the number of elements in the subgroup (n) for grouped data, the shift size value and the percentage difference between the shift size value and the mean value of the shifts calculated for each subgroup. The paper explains the proposed algorithm using examples with appropriately drawn control charts, which algorithm essentially represents an extension of the algorithm presented in ISO 7870-2:2013.

SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

2012 ◽  
Vol 12 (04) ◽  
pp. 1250083
Author(s):  
PERSHANG DOKOUHAKI ◽  
RASSOUL NOOROSSANA
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
High Performance ◽  
Discrete Data ◽  
Correlated Data ◽  
Statistical Process ◽  
Control Charting ◽  
Attribute Quality ◽  
Related Process

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

A Conceptual Methodology for Recognition of Constrained Control Chart Patterns

2013 ◽  
Vol 845 ◽  
pp. 696-700
Author(s):  
Razieh Haghighati ◽  
Adnan Hassan
Keyword(s):  
Pattern Recognition ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fault Tolerant ◽  
Constrained Control ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Control Chart Pattern ◽  
Chart Patterns

Traditional statistical process control (SPC) charting techniques were developed to monitor process status and helping identify assignable causes. Unnatural patterns in the process are recognized by means of control chart pattern recognition (CCPR) techniques. There are a broad set of studies in CCPR domain, however, given the growing doubts concerning the performance of control charts in presence of constrained data, this area has been overlooked in the literature. This paper, reports a preliminary work to develop a scheme for fault tolerant CCPR that is capable of (i) detecting of constrained data that is sampled in a misaligned uneven fashion and/or be partly lost or unavailable and (ii) accommodating the system in order to improve the reliability of recognition.

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Inspection in Process Control

1998 ◽  
Vol 42 (16) ◽  
pp. 1170-1174
Author(s):  
Somchart Thepvongs ◽  
Brian M. Kleiner
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Signal Detection ◽  
Control Chart ◽  
Control Charts ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Statistical Quality Control ◽  
Total Quality ◽  
Statistical Process

Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

An Anomaly Identification Method of Coal Gas Pre-Drainage Parameters Based on T2 Control Chart

2014 ◽  
Vol 700 ◽  
pp. 549-552
Author(s):  
Shao Jie Hou ◽  
Xian Zun Meng ◽  
Yu Wei Zhang
Keyword(s):  
Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Industrial Process ◽  
Research Field ◽  
Statistical Process ◽  
Human Errors ◽  
Important Indicator ◽  
Coal Gas ◽  
Identification Method

The T2statistic is one important indicator of statistical process control theory to identify anomalies of the multivariate industrial process. In the research field of the coal gas pre-drainage process control, previous achievements mainly based on the univariate control chart, which leaded to huge workload and facilitated some human errors. Against these problems, a more comprehensive and easy-to-use method based on the T2statistic was proposed. First at all, the basic thought and the principle of T2control chart was elaborated. Secondly, the data structure and data samples were provided after their principle component analysis. Finally, the multivariate control chart of coal gas pre-drainage process was established. Results show that the proposed anomaly identification method can integrate dozen of univariate control charts into one. Then technicians needn’t deal with many control charts in the same time and many human errors can be avoided.

Textile Processing Anomaly Detection Based on Time Domain Features and GA-SVM

2015 ◽  
Vol 740 ◽  
pp. 706-713
Author(s):  
Jian Guo Yang ◽  
Lan Xu ◽  
Zhi Jun Lu ◽  
Qian Xiang ◽  
Bin Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Process Control ◽  
Anomaly Detection ◽  
Control Chart ◽  
Time Domain ◽  
Control Charts ◽  
Detection Accuracy ◽  
Textile Processing ◽  
Statistical Process ◽  
Control Chart Pattern

Demands of automatic recognition of abnormal patterns in control charts have been increasing nowadays in manufacturing process. Control chart pattern recognition is an important statistical process control tool used to determine whether a process is run in its intended range or not and eliminate the potential attribution factors as far as possible according to the abnormal condition shown in the control chart. This paper uses the time domain features as input vector and genetic algorithm to obtain the optimal parameters of SVM in a self-adapted manner. Design anomaly detection model for dynamic process is made to realize control chart pattern recognition under the complex condition. The experimental results show that the proposed approach method has higher detection accuracy and stronger generalization ability than other methods, so it is more suitable for quality control in production field.

A MAGNITUDE-ROBUST CONTROL CHART FOR MONITORING AND ESTIMATING STEP CHANGES IN A POISSON RATE PARAMETER

2007 ◽  
Vol 14 (01) ◽  
pp. 1-19 ◽  
Author(s):  
MARCUS B. PERRY ◽  
JOSEPH J. PIGNATIELLO ◽  
JAMES R. SIMPSON
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Maximum Likelihood Estimates ◽  
Cusum Chart ◽  
Rate Parameter ◽  
Statistical Process ◽  
Wide Range ◽  
Statistical Process Control Charts ◽  
Performance Results

Statistical process control charts are intended to assist operators in detecting process changes. If a process change does occur, the control chart should detect the change quickly. If the operator is provided with an estimate as to when the process changed, the search to find the special cause can be more easily facilitated. We investigate a process-monitoring tool for Poisson count data that quickly responds to process mean count rate changes regardless of the magnitude of the change, while supplying useful diagnostic information. A likelihood ratio approach was used to develop a control chart for a permanent step change in a Poisson process rate parameter. The average run length (ARL) performance of this chart is compared to that of several Poisson cumulative sum (CUSUM) control charts. Our performance results show that the proposed chart performs better than any one CUSUM chart over a wide range of potential shift magnitudes. The proposed chart also provides maximum likelihood estimates of the time and the magnitude of the process shift. These crucial change point diagnostics can greatly enhance the special cause investigation.

scholarly journals Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Li Xue ◽  
Zhen He
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Economic Model ◽  
Preventive Maintenance ◽  
Control Charts ◽  
Loss Functions ◽  
Variable Sampling ◽  
Sampling Intervals ◽  
Optimal Values ◽  
The Mean

The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation should be monitored simultaneously; (3) quality loss function is not usually used to design economic model, which leads to a large social quality loss in the monitoring process of control chart. To eliminate these weaknesses, the economic design of the exponential weighted moving average (EWMA) control chart with variable sampling intervals (VSI) for monitoring the mean and standard deviation under preventive maintenance and Taguchi’s loss functions is proposed. The optimal values of the parameters are determined to minimize the loss-per-item in an average cycle process. In addition, a genetic algorithm is used in a numerical example to search for the optimal values of the parameters. According to the sensitivity analysis, the effect of the model parameters on the solution of the economic model is obtained. Finally, the comparison study shows that the VSI EWMA control charts designed by the joint economic model are expected to reduce loss.

scholarly journals A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 312 ◽  
Author(s):  
Muhammad Zahir Khan ◽  
Muhammad Farid Khan ◽  
Muhammad Aslam ◽  
Seyed Taghi Akhavan Niaki ◽  
Abdur Razzaque Mughal
Keyword(s):  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Fuzzy Data ◽  
Process Mean ◽  
Statistical Process ◽  
Control Charting ◽  
Real Life Data

Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

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