Statistical Process Control Tools Research Articles

Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network

The application of statistical process control (SPC) has promoted production quality improvement of many enterprises. As a core tool of SPC, control chart is used to reflect the production state. In addition to normal pattern, abnormalities in the production process can be summarized in seven basic control chart patterns (CCPs). The recognition of CCPs is helpful to identify quality failures and find root abnormal causes in time. Convolutional neural network (CNN) is a classical model in the field of deep learning. CNN can automatically extract features from the raw data, so the operation of constructing manual features can be omitted. In this paper, the one-dimensional CNN is applied to the recognition of CCPs and achieves 98.96% average recognition accuracy in 30 tests. What’s more, even if there is a deviation between the distribution of test data and training data, the model still shows excellent generalization performance.

CEP ONLINE: A WEB-ORIENTED EXPERT SYSTEM FOR STATISTICAL PROCESS CONTROL

ABSTRACT In this paper, a new software for Statistical Process Control (SPC) is proposed. The system, the so-called CEP Online, was developed based on statistical computing resources of well-known free softwares, such as HTML, PHP, R and MySQL under an online server with operating system Linux Ubuntu. The main uni and multivariate SPC tools are available for monitoring and evaluation of manufacturing and non-manufacturing production processes over time. Some advantages of the new software are: (i) low operational cost, since it is cloud-based, only needing a computer connected to the Internet; (ii) easy to use with great interaction with the user; (iii) it does not require investment in any specific hardware or software; (iv) real time reports generation on process condition monitoring and process capability. Thus, the CEP Online offers for SPC practitioners fast, efficient and accurate SPC procedures. Therefore, CEP Online becomes an important resource for those who have no access to non-free softwares, such as SAS, SPSS, Minitab and STATISTICA. To the best of our knowledge, the CEP Online is unique with respect to its characteristics.

A sequential monitoring Bayesian control scheme for attributes

ABSTRACTClassic approaches of Statistical Process Control (SPC) include the collection of samples from a production process and the computation of some output concerning a specific quality characteristic. This output, which is called chart statistic, is then compared against appropriately designed limits to decide whether an investigation for an assignable cause occurrence is needed. In recent years however, and especially in highly automated production processes, it is not unusual to have the ability of 100% inspection of the entire production. In such cases, the control procedure should be based on more appropriately designed SPC tools. This paper proposes a Bayesian model for production processes where all produced items are inspected. Each product may be conforming or nonconforming, and the output of the control procedure is the probability of out-of-control operation. This probability is compared against a critical probability to decide whether an intervention to the process is economically advisable. The case of inspection errors, namely, the case where a conforming item may be erroneously categorized as nonconforming and vice versa, is also investigated. Our numerical investigation reveals the economic superiority of the new scheme compared to simpler schemes that have been proposed over the years to tackle such problems.

On designing CUSUM charts using ratio-type estimators for monitoring the location of normal processes

Control chart is one of the important tools in statistical process control (SPC) that plays a significant role in monitoring and identifying disturbances of any production process. The Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) are commonly used control charts for detecting process shifts. CUSUM and EWMA chart are more sensitive in detecting smaller shifts whereas the typical Shewhart chart is known to be only sensitive to large process shifts. The present study incorporates ratio-type estimators based on auxiliary information in CUSUM structure as a substitute of simple mean estimator for monitoring process location. These estimators are more efficient than the simple mean estimator in the presence of high correlation between the study and the auxiliary variables. Average run length (ARL), standard deviation of run length (SDRL) and extra quadratic loss (EQL) are used to measure the performances of the proposed charts. The performance comparison of the proposed chart with the existing CUSUM, CUSUM-FIR and many other control charts are made by using out-of-control ARL. The comparison revealed the superiority of the suggested charts over the other existing charts. An illustrative example is also provided for the performance evaluation of the proposed charts.

Use of real-time extend GNSS for planting and inverting peanuts

Among the main techniques employed in precision agriculture, yield mapping and automatic guidance of agricultural machines are the best-known to farmers. The objective of this study was to evaluate, using statistical process control tools, the quality of automatic guidance using satellite signals, to reduce positioning errors and losses in peanut digging. The treatments consisted of the use of manual (operator guidance) and automatic (autopilot) guidance with RTX satellite signals in sowing and digging operations. The quality of the operation was evaluated after collection of 30 points spaced at 100 m for each quality indicator, which are the losses and the errors of alignment of the mechanised sets in sowing and digging operations. From the perspective of statistical control, manual guidance was shown to be compromised for the quality indicators of digging losses. Despite the instability in the sowing and digging operations, the use of automatic guidance proved to be accurate. The use of automatic guidance increases the precision and reduces overlaps (< 38 mm, as stipulated by the supplier) for sowing and digging. The manual sowing mean error between overlaps was stable; however, it did not remain constant over time.

On auxiliary information-based control charts for autocorrelated processes with application in manufacturing industry

Multivariate autoregressive (MAR) models are an attractive choice for applications in the processes related to finance, medical, and industry. For the monitoring of such processes, control chart is the most important and widely used tool of statistical process control tool kit. Moreover, the presence of auxiliary information helps in better estimation of different process parameters. The literature on use of auxiliary variables in control charts assumes independence of observations. In practice, we may come across processes dealing with autocorrelated outcomes. In such situations, a control chart usually produces high false alarms and exhibits slow detection of shifts when the process is out-of-control. This study intends to suggest some auxiliary information-based Shewhart charts for autocorrelated univariate and bivariate AR(1) processes. The proposed structures take into account the autocorrelation structure and offer more effective designs of control charts for efficient process monitoring. The performance measures used in this study are based on run length measures such as average run length, extra quadratic loss, relative average run length and performance comparison index. A detailed performance analysis is carried out to sort out the best performing charts. In addition, we have considered an application from a manufacturing process to demonstrate the implementation of the proposed charting structures in real scenario.

Quality monitoring of soybean seed tests using Statistical Process Control

ABSTRACT The success of soybean cultivation depends on several factors, one which is the use of high-quality seeds, which can guarantee growth of the desired plant population. The objective of this study was to monitor possible failures during the seed production process, and to determine the association between germination and tetrazolium tests through tools of Statistical Process Control (CEP). The experimental design was based on premises of the CEP, using percentage of germination, seed viability, mechanical damage, bed bug, and moisture as indicators of quality. It is possible to detect flaws in the soybean production process through control charts, as well as through germination and tetrazolium tests; results indicated that germination failures are largely caused by mechanical damage and bed bug attacks.

Investigating the impact of hard total quality management practices on operational performance in manufacturing organizations

PurposeThe purpose of this paper is to investigate the impact of hard total quality management (TQM) practices on operational performance dimensions in manufacturing organizations.Design/methodology/approachThe study was designed in the Jordanian manufacturing sector. Organizations that are familiar with hard TQM practices specifically that had already implemented ISO 9001 certificate were randomly surveyed through structured questionnaire, yielded a sample of 40 responding organizations. Principal component factor analysis was used to analyze hard TQM practices implementation and organizations operational performance dimensions. As a result, specific hard TQM practices and operational performance dimensions were extracted. The hard TQM practices that significantly influence operational performance dimensions were tested using simple and multiple linear regression analysis.FindingsThe research findings showed that hard TQM practices in manufacturing sector include; continuous improvement, statistical process control (SPC), process management and quality tools and techniques. Meanwhile, operational performance dimensions include; quality and inventory management performance. Also, the findings showed that continuous improvement and SPC practices play a major role in obtaining the desired results of operational performance.Research limitations/implicationsThree limitations can be identified in this research, using subjective measures of TQM practices and operational performance, low response rate and using a sample of organizations from various industries. This paper can help Jordanian manufacturing organizations to foster proper practices and make the right decisions related to operational performance.Originality/valueThis study describes a reliable hard TQM model that can be used by manufacturing organizations to improve their operational performance. Also, the results of this study contribute to prior literature and recommend some best practices for manufacturing organizations.

A model‐free approach to reduce the effect of autocorrelation on statistical process control charts

AbstractThe presence of autocorrelation in time series data significantly affects the performance of statistical process control charts. For process data collected in real time, consecutive observations result in serial dependence that violates the assumption of sample independence. This paper focuses on the improvement of statistical process control (SPC) charts of individual observations when they are designed to monitor stationary processes in the presence of data autocorrelation. Three widely used SPC tools, Shewhart chart, Hotelling's T2 chart, and PCA‐based SPC charts, are chosen to demonstrate the proposed approach. There has been a modified version of SPC chart shown to be effective in accommodating the autocorrelation behavior, relying on a model‐based estimation of chart parameters. Process model estimation, however, is a challenging task, especially for complex systems. In this paper, a model‐free monitoring approach is proposed for SPC charts of individual observations, aimed at reducing the effect of autocorrelation on chart performance. It relies on a skipping strategy that creates sample bins with little or no autocorrelation. A single skipping chart and a combined skipping chart (CSC) are established, and a parameter sensitivity study is performed through simulations. In the case studies, the advantages of the CSC are noted as being a model‐free approach and having a performance consistent with that of the benchmark modified chart.

Joint Economic Design of CUSUM Control Chart and Age-Based Imperfect Preventive Maintenance Policy

The close relationship between statistical process control and maintenance has attracted lots of researchers to focus on the jointly economic design of control chart (a main tool of statistical process control) and preventive maintenance policy, and much progress has been made in this field. However, in the existing literatures, the X- chart is used most, and other charts are rarely considered. In this paper, the economic design of CUSUM chart and age-based imperfect preventive maintenance policy is presented. The process is considered as a multiphase system, and a recursive algorithm is used to model each phase. Besides, a sampling policy under the non-Markovian deterioration assumption is employed, and an age-based imperfect preventive maintenance policy is used. An optimization model with the objective of minimizing the expected cost per unit time is constructed to obtain the near-optimal solution of decision variables: the age of the machine for maintenance, the number of age-based maintenances, sample size, sampling intervals, and the decision interval coefficient and reference value coefficient of CUSUM chart. The solution procedure of the model is provided. Also, sensitivity analysis is performed on the decision variables for each of the various parameters.

A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances

With rapid advances in sensing technology and data acquisition systems, high‐dimensional data appear in many settings. The high dimensionality presents a new challenge to the traditional tools in multivariate statistical process control, due to the “curse of dimensionality.” Various tests for mean vectors in high dimensional situations have been discussed recently; however, they have been rarely adapted to process monitoring. This paper develops a distribution‐free control chart based on interpoint distances for monitoring mean vectors in high‐dimensional settings. Other than the Euclidean distance, the family of Minkowski distance is used for discussion, which is a generalization of the former and other distances. The proposed approach is very general as it represents a class of distribution‐free control charts based on distances. Numerical results show that the proposed control chart is efficient in detecting mean shifts in both symmetric and heavy‐tailed distributions.

Comparing Short-Memory Charts to Monitor the Traffic Intensity of Single Server Queues

AbstractThis paper describes the application of simple quality control charts to monitor the traffic intensity of single server queues, a still uncommon use of what is arguably the most successful statistical process control tool. These charts play a vital role in the detection of increases in the traffic intensity of single server queueing systems such as the{M/G/1},{GI/M/1}and{GI/G/1}queues. The corresponding control statistics refer solely to a customer-arrival/departure epoch as opposed to several such epochs, thus they are termed short-memory charts. We compare the RL performance of those charts under three out-of-control scenarios referring to increases in the traffic intensity due to: a decrease in the service rate while the arrival rate remains unchanged; an increase in the arrival rate while the service rate is constant; an increase in the arrival rate accompanied by a proportional decrease in the service rate. These comparisons refer to a broad set of interarrival and service time distributions, namely exponential, Erlang, hyper-exponential, and hypo-exponential. Extensive results and striking illustrations are provided to give the quality control practitioner an idea of how these charts perform in practice.

Implementation of Statistical Process Control (SPC) in the Sewing Section of Garment Industry for Quality Improvement

Abstract The main aim of this research was to implement appropriate Statistical Process Control (SPC) techniques for quality characteristics on sewing floor of garment Industry. Among different SPC quality improvements tools, control charts have been selected. After analyzing and selecting different critical parameters based on company and customer requirements, the X-bar and R charts for variable and c-charts for attribute quality characteristics have been identified and implemented in the trouser sewing lines for quality improvement. The check points for selected control chart implementation have also been designed. Remedial action plans for the occurred special cause variations and process stability were developed. The project incorporated theoretical and on-job training schemes for different quality team members, to understand the SPC concept and its implementation procedure. After implementation, significant improvements in the sewing section were achieved. The four months analysis before and after implementation of the SPC tools showed that the rejection percentage was reduced from 9.141% to 6.4%. Successful implementation of the result of this project can significantly improve process performance of other similar manufacturing units with appropriate modification.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance.

Design and implementation of SPC systems in defense industry manufacturing process

In this paper Statistical Process Control (SPC) was proposed for manufacturing process of a defense product manufacturing company, with the aim of improvement product quality. The production process was monitored from material input to the final output. The processes were mapped and SPC system was proposed. The system was tested by collecting data at the collecting points and analyzed by appropriate SPC tool such as: X R chart,( X) S chart and the p-chart. The results show that the processes in the some parts in production are in the under control, processes are capable, but not centered, also some sub-processes of production are not in the control but also not capable.

Determination of Factors and Quality Control of Car Painting Based on FMEA and SPC.V2

The global automotive industry demands world-class levels of product quality, productivity, and competitiveness as well as continual improvement. To achieve this goal, many vehicle manufacturers companies use quality control tools to improve the quality of the product with zero defects and highly satisfied to the customer. Nowadays, there are a lot of quality tools applied to solve the problem quickly but it’s still the fact to find out good and efficient solving way. The study emphasizes the identification of potential failure which failures may have encountered in the production process and it will lead to car scrap, rework and influence of the internal production and quality target. After the complete study of the manufacturing process and production data-failure causes, failure rate and data etc. FMEA discover the weak processes in the form of higher risk priority number in the manufacturing of the car painting process, which required reducing by identifying and implementing of the defects and this will improve the process quality of the painting surface of the car. To analysis, the improvement of the car surface defects SPC (Statistical Process Control) tools are more efficient where can easily visible the defects trends. SPC chart is chronological graphs of process data that are used in manufactures industries to help understand, control and improve the process and that although based on statistical theory area easy for practitioners to use and interpret. In order to orient goal of zero defects of the car, surface uses the PFMEA technique to prioritize the defects and statistically analyze the roots cause of the defect and control the defects through continues improvements process.

Statistical process control and strength monitoring of ready-mixed concrete through the application of CUSUM and EWMA control charts: a case study of Ahmedabad and Gandhinagar RMC plants

This research work is an attempt to improve the quality of concrete by implementing statistical process control tools like CUSUM with V-mask and exponentially weighted moving average (EWMA) control charts for ready-mixed concrete (RMC) plants in the Gandhinagar and Ahmedabad regions of India. These tools are used for sensitising the qualitative analysis of concrete in terms of the 28 days cube compressive strength. It has been observed that the RMC plant authorities were using an over-conservative mix design for the grades of concrete produced. This practice resulted in the usage of more cement than that stipulated in the mix design. The usage of higher cement content is wastage of resource. Also, this practice results in adverse side effects like production of more heat of hydration which creates shrinkage cracks. The application of these tools for strength monitoring would result in cost savings of about INR 4.8 million to 7.2 million per annum.

Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending

Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending

The Use of Statistical Process Control Tools for Analysing Financial Statements

Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.