On-line Statistical Process Control Research Articles

V‐model with fuzzy quality function deployments for mobile application development

AbstractMobile application (app) development is a subset of software development distinguished by a short development life cycle, rapidly changing business requirements, mobile device technical specifications, and operating system compatibility. The study presents a V‐model mobile app development technique that combines two fuzzy quality function deployment (FQFD) phases. In mobile app development, user requirements are often misunderstood, resulting in poor mobile app design. The research motivation for employing FQFD is to obtain more accurate deductions of human (user or design team) inputs affected by intrinsic vagueness and ambiguity in linguistics. The combination hence delivers synergistic benefits, where FQFDs more effectively solicits and breaks down the voice of the customer (VOC) and system requirements, and the V‐model aligns the development to the broken‐down requirements. The development of an online statistical process control app was demonstrated. FQFD structurally related user requirements, system requirements, and design strategies in the V‐model's verification phases. The validation phases used test plans created during the verification phases to detect and correct programming errors. In the final validation stage, the focus group agreed that the mobile app met all of the user requirements. This study adds to the methodology's novelty by proposing and demonstrating the integration of the FQFD and V‐model into the development of mobile apps. As a result of the limitation, other areas of software engineering, such as time and resource coordination and release management, are not considered.

A registration-free approach for statistical process control of 3D scanned objects via FEM

Recent work in on-line Statistical Process Control (SPC) of manufactured 3-dimensional (3-D) objects has been proposed based on the estimation of the spectrum of the Laplace-Beltrami (LB) operator, a differential operator that encodes the geometrical features of a manifold and is widely used in Machine Learning (i.e., Manifold Learning). The resulting spectra are an intrinsic geometrical feature of each part, and thus can be compared between parts to assess the state of the manufacturing process while avoiding part to part registration (or “part localization”) pre-processing or the need for equal size meshes, characteristics which are required in previous approaches for SPC of 3D parts. The recent spectral SPC methods, however, are limited to monitoring surface data from objects such that the scanned meshes have no boundaries, holes or missing portions. In this paper we extend spectral methods by first considering a more accurate and general estimator of the LB spectrum that is obtained by application of Finite Element Methods (FEM) to the solution of Helmholtz's equation with boundaries. It is shown how the new spectral FEM approach, while it retains the advantages of not requiring part localization/registration or equal size datasets scanned from each part, it provides more accurate spectrum estimates, which results in faster detection of out of control conditions than earlier methods, can be applied to both mesh or volumetric (solid) scans, and furthermore, it is shown how it can be applied to partial scans that result in open meshes (surface or volumetric) with boundaries, increasing the practical applicability of the methods. The present work brings SPC methods closer to contemporary research in Computer Graphics and Manifold Learning. MATLAB code that reproduces the examples of this paper is provided in the supplementary materials.

Distribution-Free CUSUM-Type Control Charts for Monitoring Industrial Processes: An Overview

In the present paper we provide an up-to-date overview of nonparametric Cumulative Sum (CUSUM) monitoring schemes. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output does not match to a particular distributional model. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric CUSUM-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online Statistical Process Control.

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

Optimisation of the Turbo Blowers cooling process functionality using OSPC

With the start of a new industrial revolution, meaning the forth level of automation, that includes data lake integration on a local server or cloud options, the need for analysing the gathered data has emerged. The classic methods of statistical analysis are now applied on data collected from servers, and the online statistical approach has emerged by using an Online Statistical Process Control (OSPC) tool. The paper aims to present the statistical methods used for Turbo Blowers optimisation of the cooling process of steam in a closed water-steam circuit, in the metallurgy industry. By using the OSPC connected to a server, data was gathered and processed in order to optimise the cooling process of the steam, in order to provide the end user with the required amount of compressed air without jeopardising the equipment.

Recognition of unnatural patterns in process control charts through combining two types of neural networks

Unnatural patterns in control charts associate a set of assignable causes in production process. Therefore, effective discrimination of these patterns has special importance in on-line statistical process control. In resent years, artificial neural networks because of their abilities in patterns recognition have been used to detect control charts patterns. The correct and precise recognition in a real-time is difficult, because control chart patterns are distorted by common cause variations that occur naturally in the manufacturing processes. Nearly the most of investigations based neural networks solely have emphasized the control chart patterns recognition and have not considered extraction of detailed information that is important for analysis of assignable causes. Moreover, some of the patterns simulator functions do not represent completely the real world behavior. This study presents a hybrid model, including two types of neural networks. The proposed model can detect and discriminate single and concurrent patterns, determine the major corresponding parameters and estimate the starting point of unnatural patterns. This model introduces a procedure for patterns simulation in variable and attribute qualitative characteristics control charts. In addition, this research improves and develops unnatural patterns generator functions. Numerical results indicate that the proposed model performs suitably and effectively.

An improved scheme for online recognition of control chart patterns

This paper proposes two alternative schemes for the online recognition of control chart patterns (CCPs), namely: 1) a scheme based on direct continuous recognition; 2) a scheme based on 'recognition only when necessary'. The study focuses on recognition of six CCPs plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trend-down and cyclic. The artificial neural network (ANN) recogniser used was based on multilayer perceptrons (MLPs) architecture. The performance of the schemes was evaluated based on percentage correct recognition, average run lengths (ARL) and average recognition attempts (ARA). The findings suggest that the online recognition should be made only when necessary. Continuous recognition is not only wasteful, but also results in poorer results. The methodology proposed in this study is a step forward in realising a truly automated and intelligent online statistical process control chart pattern recognition system.

On-line SPC with consideration of learning curve

This work identifies a link between on-line statistical process control (SPC) and the learning effect for the process standard deviation (PSD) caused by the quality improvement (QI) program. The learning curve (LC) is used to describe and forecast, and the exponentially weighted root mean square control chart is used to monitor the progress in reducing PSD. A modification of the quality control chart (QCC) that considers LC of PSD is proposed. The reduction rate of PSD may be large during the initial stage of the QI program, and influences QCC construction. Simulation is used to compare the shift-detecting ability of the Shewhart- X ¯ control chart and EWMA- X ¯ control chart, without- and with- consideration of LC. The EWMA- X ¯ chart with consideration of LC performs best. In comparison, the Shewhart- X ¯ chart without LC consideration has almost no shift-detecting ability when the shift magnitude of the process mean is small, leading to rendering quality control ineffective.

The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter?

Much work has been devoted to the on-line multivariate statistical process control (MSPC) of batch processes based on bilinear models such as principal component analysis (PCA). The data collected from a batch process have 3-way structure and they have to be arranged in 2-way matrices prior to PCA. Several transformation methods have been proposed reporting contradictory conclusions regarding monitoring performance. The aim of this paper is to run an objective research to assess the influence of 3-way to 2-way transformations on the monitoring performance. Batch-wise, variable-wise, batch-dynamic, local and multi-phase approaches are compared using simulated data from a pair of ‘toy’ processes with quite different and perfectly known dynamics. Several realistic types of faults are simulated. The main conclusions regarding fault detection performance are: (i) the best monitoring approach depends more on the type of fault than on the process dynamics; (ii) the information considered in the model structure will determine which faults are detectable; (iii) in general, a good monitoring performance is achieved with a parsimonious model; (iv) proper identification of the changes in the correlation structure -the phases of the process- is crucial for parsimonious models; (v) it can be advisable to combine several unfolding methods to improve the detection ability of predefined types of faults. These results were coherently explained from the theoretically known features of the models and validate the conclusions in previous investigations with real batch processes and realistic simulations.

Construction of p-charts using degree of nonconformity

Control charts are among the simplest of on-line statistical process control techniques. When the quality characteristic is a variable, the p-chart takes time to react to shifts in the production process because of its weak response to small variations in the process mean and variance. In this paper, instead of considering an item to be either conforming or nonconforming, the “degree of nonconformity” based on fuzzy concepts is defined and a fuzzy p-chart based on the mean degree of nonconformity is constructed. It is shown that this chart has a better response to variations in both the mean and the variance of the process.

Construction of Control Charts Using Fuzzy Multinomial Quality

Control charts are the simplest type of on-line statistical process control techniques. One of the basic control charts is p -chart. In classical p -charts, each item classifies as either nonconforming or conforming to the specification with respect to the quality characteristic. In practice, one may classify each item in more than two categories such as bad, medium, good, and excellent. Based on this, we introduce a fuzzy multinomial chart ( FM -chart) for monitoring a multinomial process. Control limits of FM -chart are obtained by using the multinomial distribution and the degrees of membership which one assigned to the distinct categories. The comparison of the FM -chart and the p -chart based on a food production process illustrates that the FM -chart leads to better results.

Framework for regression‐based missing data imputation methods in on‐line MSPC

AbstractMissing data are a critical issue in on‐line multivariate statistical process control (MSPC). Among the different scores estimation methods for future multivariate incomplete observations from an existing principal component analysis (PCA) model, the most statistical efficient ones are those that estimate the scores for the new incomplete observation as the prediction from a regression model. We have called them regression‐based methods. Several approximations have been proposed in the literature to overcome the singularity or ill‐conditioning problems that some of the mentioned methods can suffer due to missing data. This is particularly acute in on‐line batch process monitoring. In order to ease the comparison of the statistical performance of these methods and to improve the understanding of their relationships, in this paper we propose a framework that allows to write these regression‐based methods by an unique expression, function of a key matrix. From this framework a statistical performance index (PRESV) is introduced as a way to compare the statistical efficiency of the different framework members and to predict the impact of specific missing data combinations on scores estimation without requiring real data. The results are illustrated by application to several continuous and batch industrial data sets. Copyright © 2005 John Wiley & Sons, Ltd.

On-line monitoring of a sugar crystallization process

The present paper reports a comparative evaluation of four multivariate statistical process control (SPC) techniques for the on-line monitoring of an industrial sugar crystallization process. The process itself is challenging since it is carried out in multiple phases and there exists strong non-linear and dynamic effects between the variables. The methods investigated include classical on-line univariate statistical process control, batch dynamic principal component analysis (BDPCA), moving window principal component analysis (MWPCA), batch observation level analysis (BOL) and time-varying state space modelling (TVSS). The study is focused on issues of on-line detection of changes in crystallization process operation, the early warning of process malfunctions and potential production failures; problems that have not been directly addressed by existing statistical monitoring schemes. The results obtained demonstrate the superior performance of the TVSS approach to successfully detect abnormal events and periods of bad operation early enough to allow bad batches and related losses in amounts of recycled sucrose to be significantly reduced.

An empirical bayesian stopping rule in testing and verification of behavioral models

Software stopping rules are tools to effectively minimize the time and cost involved in software testing. The algorithms serve to guide the testing process such that if a certain level of branch or fault (or failure) coverage is obtained without the expectation of further significant coverage, then the testing strategy can be stopped or changed to accommodate further, more advanced testing strategies. By combining cost analysis with a variety of stopping-rule algorithms, a comparison can be made to determine an optimally cost-effective stopping point. A novel cost-effective stopping rule using empirical Bayesian principles for a nonhomogeneous Poisson counting process compounded with logarithmic-series distribution (LSD) is derived and satisfactorily applied to digital software testing and verification. It is assumed that the software failures or branches covered, whichever the case may be, clustered at the application of a given test-case are positively correlated, i.e., contagious, implying that the occurrence of one software failure (or coverage of a branch) positively influences the occurrence of the next. This phenomenon of clustering of software failures or branch coverage is often observed in software testing practice. The r.v. w/sub i/ of the failure-clump size of the interval is assumed to have LSD(/spl theta/) and justified on the data sets by employing a chi-square goodness of fit testing while the distribution of the number of test cases is Poisson(/spl lambda/). Then, the distribution of the total number of observed failures, or similarly covered branches, X is a compound Poisson /sup /spl and// LSD, i.e., negative binomial distribution, given that a certain mathematical identity holds. For each checkpoint in time, either the software satisfies a desired reliability attached to an economic criterion, or else the software testing is allowed to continue. By using a one-step-look-ahead formula derived for the model, the proposed stopping rule is applied to five test case-based data sets acquired by testing embedded chips through the complex VHDL models. Further, multistrategy testing is conducted to show its superiority to single-stage testing. Results are satisfactorily interpreted from a practitioner's viewpoint as an innovative alternative to the ubiquitous test-it-to-death approach, which is known to waste billions of test cases in a tedious process of finding more bugs. Moreover, the proposed dynamic stopping-rule algorithm can validly be employed as an alternative paradigm to the existing on-line statistical process control methods static in nature for the manufacturing industry, provided that underlying statistical assumptions hold. A detailed comparative literature survey of stopping-rule methods is also included in terms of pros and cons, and cost effectiveness.

Design of double- and triple-sampling X-bar control charts using genetic algorithms

As today's manufacturing firms are moving towards agile manufacturing, quick and economic on-line statistical process control solutions are in high demand. Multiple sampling X-bar control charts are such an alternative. They can be designed to allow quick detection of a small shift in process mean and provide a quick response in an agile manufacturing environment. In this paper, the designs of double-sampling (DS) X-bar control charts are formulated and solved with a genetic algorithm. Based on the results in solving the DS chart design problems, triple sampling (TS) X-bar control charts are developed. The efficiency of the TS charts is compared with that of the DS charts. The results of the comparison show that TS charts are more efficient in terms of minimizing the average sample size.

The GLRT for statistical process control of autocorrelated processes

This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests - a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the GLRT performance is far superior to either a CUSUM or Shewhart test, while for others the difference is negligible or the CUSUM test performs slightly better. Simple, intuitive guidelines are provided for determining which residual-based test to use. Additional advantages of the GLRT are that it directly provides estimates of the magnitude and time of occurrence of the mean shift, and can be used to distinguish different types of faults, e.g., a sustained mean shift versus a temporary spike.

IntelliSPC: a hybrid intelligent tool for on-line economical statistical process control

Statistical process control (SPC) has become one of the most commonly used tools, for maintaining an acceptable and stable level of quality characteristics in today's manufacturing. With the movement towards a computer integrated manufacturing (CIM) environment, computer based algorithms need to be developed to implement the various SPC tasks automatically. This paper presents a hybrid intelligent tool (IntelliSPC) in which a neural network based control chart pattern recognition system, an expert system based control chart alarm interpretation system and a quality cost simulation system were integrated for on-line SPC. IntelliSPC was designed to provide the quality practitioners with the status of the process (in-control or out-of-control), the plausible causes for the out-of-control situation and cost-effective actions against the out-of-control situation. This tool was intended to be implemented in a scenario where sample data are being collected on-line by automated inspection devices and monitored by control charts. An implementation example is provided to demonstrate how the proposed hybrid system could be usefully applied in a real-world automated production line. This work confirms the potential synergies of hybrid artificial intelligence (AI) techniques in a complex problem solving procedure, such as an automated SPC scheme.

A review of neural networks for statistical process control

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.

On the usage of spreadsheet packages for automating statistical process control tasks

This paper presents an implementation of statistical process control application using a modern spreadsheet software package. This implementation uses a graphical user interface and integrates the SPC functionality into the spreadsheet package in such form that it becomes an integral part of the package. A fairly complete collection of control chart types have been included. This system also contains an embedded expert for the selection of the appropriate control chart type for the problem depending on the data characteristics. The usage of this statistical process control application is demonstrated by using on-line statistical process control of control loop operation as an example. This example clearly illustrates the importance of variable selection for performing statistical process control.

On-line diagnostic monitoring of photoresist ashing

On-line statistical process control (SPC) has been implemented on a single-wafer remote microwave plasma photoresist asher. SPC for ashing is made more difficult because the prior processes, e.g., ion implantation, affect the properties of the resist material, and consequently the ashing behavior. The system presented comprehends the variety of incoming wafer states from a complex process flow. On-line SPC charts track photoresist clear time on a wafer-to-wafer basis using optical emission spectroscopy. The data is corrected for the "first-wafer" effect, whereby the clear time for a wafer decreases as the delay time between ashing wafers increases. The data is standardized using an expected time and variance for each process flow level to allow all results to be presented in a single set of individuals and moving-standard deviation Shewhart charts. Standard SPC rules are applied automatically within each process flow level to test for unnatural variation in the data. Observed abnormal behavior is due mainly to changes in the incoming material for a specific process flow level, not deviations in the ashing process. When a shift in incoming wafer state is detected, the expected response for that process level is automatically updated to reflect the change. The usefulness of on-line monitoring as a means for identifying misprocessing at prior process steps has been demonstrated, Early diagnosis can save money by avoiding expensive downstream processing on previously misprocessed wafers, In our demonstration laboratory, the equipment has processed wafers from a dozen process flow levels.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>