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Vännman, Kerstin
Publications (10 of 74) Show all publications
Tano, I. A. & Vännman, K. (2013). A multivariate process capability index based on the first principal component only (ed.). Paper presented at . Quality and Reliability Engineering International, 29(7), 987-1003
Open this publication in new window or tab >>A multivariate process capability index based on the first principal component only
2013 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 7, p. 987-1003Article in journal (Refereed) Published
Abstract [en]

Often the quality of a process is determined by several correlated univariate variables. In such cases, the considered quality characteristic should be treated as a vector. Several different multivariate process capability indices (MPCIs) have been developed for such a situation, but confidence intervals or tests have been derived for only a handful of these. In practice, the conclusion about process capability needs to be drawn from a random sample, making confidence intervals or tests for the MPCIs important. Principal component analysis (PCA) is a well-known tool to use in multivariate situations. We present, under the assumption of multivariate normality, a new MPCI by applying PCA to a set of suitably transformed variables. We also propose a decision procedure, based on a test of this new index, to be used to decide whether a process can be claimed capable or not at a stated significance level. This new MPCI and its accompanying decision procedure avoid drawbacks found for previously published MPCIs with confidence intervals. By transforming the original variables, we need to consider the first principal component only. Hence, a multivariate situation can be converted into a familiar univariate process capability index. Furthermore, the proposed new MPCI has the property that if the index exceeds a given threshold value the probability of non-conformance is bounded by a known value. Properties, like significance level and power, of the proposed decision procedure is evaluated through a simulation study in the two-dimensional case. A comparative simulation study between our new MPCI and an MPCI previously suggested in the literature is also performed. These studies show that our proposed MPCI with accompanying decision procedure has desirable properties and is worth to study further

National Category
Probability Theory and Statistics
Research subject
Matemathical Statistics
Identifiers
urn:nbn:se:ltu:diva-15915 (URN)10.1002/qre.1451 (DOI)000333581600005 ()2-s2.0-84887118097 (Scopus ID)f7e5566d-cf5f-4a99-a6c6-e5c9cb662fde (Local ID)f7e5566d-cf5f-4a99-a6c6-e5c9cb662fde (Archive number)f7e5566d-cf5f-4a99-a6c6-e5c9cb662fde (OAI)
Note
Validerad; 2013; 20120904 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Vanhatalo, E., Bergquist, B. & Vännman, K. (2013). Towards improved analysis methods for two-level factorial experiments with time series responses (ed.). Paper presented at . Quality and Reliability Engineering International, 29(5), 725-741
Open this publication in new window or tab >>Towards improved analysis methods for two-level factorial experiments with time series responses
2013 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 5, p. 725-741Article in journal (Refereed) Published
Abstract [en]

Dynamic processes exhibit a time delay between the disturbances and the resulting process response. Therefore, one has to acknowledge process dynamics, such as transition times, when planning and analyzing experiments in dynamic processes. In this article, we explore, discuss, and compare different methods to estimate location effects for two-level factorial experiments where the responses are represented by time series. Particularly, we outline the use of intervention-noise modeling to estimate the effects and to compare this method by using the averages of the response observations in each run as the single response. The comparisons are made by simulated experiments using a dynamic continuous process model. The results show that the effect estimates for the different analysis methods are similar. Using the average of the response in each run, but removing the transition time, is found to be a competitive, robust, and straightforward method, whereas intervention-noise models are found to be more comprehensive, render slightly fewer spurious effects, find more of the active effects for unreplicated experiments and provide the possibility to model effect dynamics.

National Category
Reliability and Maintenance Probability Theory and Statistics
Research subject
Quality Technology and Management; Matemathical Statistics
Identifiers
urn:nbn:se:ltu:diva-10653 (URN)10.1002/qre.1424 (DOI)000322222600012 ()2-s2.0-84880841527 (Scopus ID)97bbb1f9-2ed2-495a-a577-e73dab2809aa (Local ID)97bbb1f9-2ed2-495a-a577-e73dab2809aa (Archive number)97bbb1f9-2ed2-495a-a577-e73dab2809aa (OAI)
Note
Validerad; 2013; 20120423 (erivan)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Lundkvist, P., Vännman, K. & Kulahci, M. (2012). A Comparison of Decision Methods for Cpk When Data are Autocorrelated (ed.). Paper presented at . Quality Engineering, 24(4), 460-472
Open this publication in new window or tab >>A Comparison of Decision Methods for Cpk When Data are Autocorrelated
2012 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 24, no 4, p. 460-472Article in journal (Refereed) Published
Abstract [en]

In many industrial applications, autocorrelated data are becoming increasingly common due to, for example, on-line data collection systems with high-frequency sampling. Therefore the basic assumption of independent observations for process capability analysis is not valid. The purpose of this article is to compare decision methods using the process capability index Cpk, when data are autocorrelated. This is done through a case study followed by a simulation study. In the simulation study the actual significance level and power of the decision methods are investigated. The outcome of the article is that two methods appeared to be better than the others.

National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
urn:nbn:se:ltu:diva-10842 (URN)10.1080/08982112.2012.710165 (DOI)000309123100003 ()2-s2.0-84867036649 (Scopus ID)9b84bc86-6817-4134-bc6a-cbd60fb9011e (Local ID)9b84bc86-6817-4134-bc6a-cbd60fb9011e (Archive number)9b84bc86-6817-4134-bc6a-cbd60fb9011e (OAI)
Note
Validerad; 2012; 20120810 (pedlun)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Tano, I. A. & Vännman, K. (2012). Comparing confidence intervals for multivariate process capability indices (ed.). Paper presented at . Quality and Reliability Engineering International, 28(4), 481-495
Open this publication in new window or tab >>Comparing confidence intervals for multivariate process capability indices
2012 (English)In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 28, no 4, p. 481-495Article in journal (Refereed) Published
Abstract [en]

Multivariate process capability indices (MPCIs) are needed for process capability analysis when the quality of a process is determined by several univariate quality characteristics that are correlated. There are several different MPCIs described in the literature, but confidence intervals have been derived for only a handful of these. In practice, the conclusion about process capability must be drawn from a random sample. Hence, confidence intervals or tests for MPCIs are important. With a case study as a start and under the assumption of multivariate normality, we review and compare four different available methods for calculating confidence intervals of MPCIs that generalize the univariate index Cp. Two of the methods are based on the ratio of a tolerance region to a process region, and two are based on the principal component analysis. For two of the methods, we derive approximate confidence intervals, which are easy to calculate and can be used for moderate sample sizes. We discuss issues that need to be solved before the studied methods can be applied more generally in practice. For instance, three of the methods have approximate confidence levels only, but no investigation has been carried out on how good these approximations are. Furthermore, we highlight the problem with the correspondence between the index value and the probability of nonconformance. We also elucidate a major drawback with the existing MPCIs on the basis of the principal component analysis. Our investigation shows the need for more research to obtain an MPCI with confidence interval such that conclusions about the process capability can be drawn at a known confidence level and that a stated value of the MPCI limits the probability of nonconformance in a known way

National Category
Probability Theory and Statistics
Research subject
Matemathical Statistics
Identifiers
urn:nbn:se:ltu:diva-4753 (URN)10.1002/qre.1250 (DOI)000304152600011 ()2-s2.0-84861346948 (Scopus ID)2be073df-0bd8-45eb-9414-9b5dab50adc6 (Local ID)2be073df-0bd8-45eb-9414-9b5dab50adc6 (Archive number)2be073df-0bd8-45eb-9414-9b5dab50adc6 (OAI)
Note
Validerad; 2012; 20111019 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Vanhatalo, E., Kvarnström, B., Bergquist, B. & Vännman, K. (2011). A method to determine transition time for experiments in dynamic processes (ed.). Paper presented at . Quality Engineering, 23(1), 30-45
Open this publication in new window or tab >>A method to determine transition time for experiments in dynamic processes
2011 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 1, p. 30-45Article in journal (Refereed) Published
Abstract [en]

Process dynamics is an important consideration during the planning phase of designed experiments in dynamic processes. After changes of experimental factors, dynamic processes undergo a transition time before reaching a new steady state. To minimize experimental time and reduce costs and for experimental design and analysis, knowledge about this transition time is important. In this article, we propose a method to analyze process dynamics and estimate the transition time by combining principal component analysis and transfer function-noise modeling or intervention analysis. We illustrate the method by estimating transition times for a planned experiment in an experimental blast furnace.

National Category
Reliability and Maintenance Probability Theory and Statistics
Research subject
Quality Technology and Management; Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-4433 (URN)10.1080/08982112.2010.495099 (DOI)000299335200004 ()2-s2.0-78649919229 (Scopus ID)26082cb0-d2bf-11de-bae5-000ea68e967b (Local ID)26082cb0-d2bf-11de-bae5-000ea68e967b (Archive number)26082cb0-d2bf-11de-bae5-000ea68e967b (OAI)
Note
Validerad; 2011; 20091116 (bjokva)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Albing, M. & Vännman, K. (2011). Elliptical safety region plots for Cpk (ed.). Paper presented at . Journal of Applied Statistics, 38(6), 1169-1187
Open this publication in new window or tab >>Elliptical safety region plots for Cpk
2011 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 38, no 6, p. 1169-1187Article in journal (Refereed) Published
Abstract [en]

The process capability index Cpk is widely used when measuring the capability of a manufacturing process. A process is defined to be capable if the capability index exceeds a stated threshold value, e.g. Cpk4/3. This inequality can be expressed graphically using a process capability plot, which is a plot in the plane defined by the process mean and the process standard deviation, showing the region for a capable process. In the process capability plot, a safety region can be plotted to obtain a simple graphical decision rule to assess process capability at a given significance level. We consider safety regions to be used for the index Cpk. Under the assumption of normality, we derive elliptical safety regions so that, using a random sample, conclusions about the process capability can be drawn at a given significance level. This simple graphical tool is helpful when trying to understand whether it is the variability, the deviation from target, or both that need to be reduced to improve the capability. Furthermore, using safety regions, several characteristics with different specification limits and different sample sizes can be monitored in the same plot. The proposed graphical decision rule is also investigated with respect to power

National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-4824 (URN)10.1080/02664763.2010.491858 (DOI)000288670000005 ()2-s2.0-79952951300 (Scopus ID)2d0af12b-5af8-4586-9a52-46c641c5f54d (Local ID)2d0af12b-5af8-4586-9a52-46c641c5f54d (Archive number)2d0af12b-5af8-4586-9a52-46c641c5f54d (OAI)
Note
Validerad; 2011; 20110408 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Kvarnström, B., Bergquist, B. & Vännman, K. (2011). RFID to improve traceability in continuous granular flows: an experimental case study (ed.). Paper presented at . Quality Engineering, 23(4), 343-357
Open this publication in new window or tab >>RFID to improve traceability in continuous granular flows: an experimental case study
2011 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 4, p. 343-357Article in journal (Refereed) Published
Abstract [en]

Traceability is important for identifying the root-causes of production related quality problems. Traceability can often be reached by adding identification markers on products, but this is not a solution when the value of the individual product is much lower than the incurred cost of a marking system. This is the case for continuous production of granular media. The use of Radio Frequency Identification (RFID) technique to achieve traceability in continuous granular flows has been proposed in the literature. We study through experiments different methods to improve the performance of such an RFID system. For example, larger transponders and multiple readers are shown to improve the RFID system performance.

National Category
Reliability and Maintenance Probability Theory and Statistics
Research subject
Quality Technology and Management; Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-14093 (URN)10.1080/08982112.2011.602278 (DOI)000299336200003 ()2-s2.0-80052325697 (Scopus ID)d6937e18-da5a-40ce-a9e8-7489dfea7e88 (Local ID)d6937e18-da5a-40ce-a9e8-7489dfea7e88 (Archive number)d6937e18-da5a-40ce-a9e8-7489dfea7e88 (OAI)
Note
Validerad; 2011; 20110309 (bjarne_b)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Vanhatalo, E., Kvarnström, B., Bergquist, B. & Vännman, K. (2009). A method to determine transition time for experiments in dynamic processes (ed.). Paper presented at ENBIS Goteborg : 20/09/2009 - 24/09/2009. Paper presented at ENBIS Goteborg : 20/09/2009 - 24/09/2009.
Open this publication in new window or tab >>A method to determine transition time for experiments in dynamic processes
2009 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

Planning, conducting, and analyzing experiments performed in dynamic processes, such as continuous processes, highlight issues that the experimenter needs to consider, for example, process dynamics (inertia) and the multitude of responses. Dynamic systems exhibit a delay (transition time) the change of an experimental factor and when the response is affected. The transition time affects the required length of each experimental run in dynamic processes and long transition times may call for restrictions of the randomization of runs. By contrast, in many processes in parts production this change is almost immediate. Knowledge about the transition time helps the experimenter to avoid experimental runs that are either too short for a new steady-state to be reached, and thus incorrect estimation of treatment effects, or unnecessarily long and costly. Furthermore, knowing the transition time is important during analysis of the experiment.Determining the transition time in a dynamic process can be difficult since the processes often are heavily instrumented with a multitude of responses. The process responses are typically correlated and react to the same underlying events. Hence, multivariate statistical tools such as principal component analysis (PCA) are often beneficial during analysis. Furthermore, the responses are often highly positively autocorrelated due to frequent sampling. We propose a method to determine the transition time between experimental runs in a dynamic process. We use PCA to summarize the systematic variation in a multivariate response space. The time series analysis techniques ‘transfer function-noise modeling' or ‘intervention analysis' are then used to model the dynamic relation between an input time series event and output time series response using the principal component scores. We illustrate the method by estimating the transition time for treatment changes in an experimental blast furnace. This knowledge provides valuable input to the planning and analysis phase of the experiments in the process.

National Category
Reliability and Maintenance Probability Theory and Statistics
Research subject
Quality Technology and Management; Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-29958 (URN)399419b0-e499-11de-bae5-000ea68e967b (Local ID)399419b0-e499-11de-bae5-000ea68e967b (Archive number)399419b0-e499-11de-bae5-000ea68e967b (OAI)
Conference
ENBIS Goteborg : 20/09/2009 - 24/09/2009
Note
Godkänd; 2009; 20091209 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-03-26Bibliographically approved
Castagliola, P., Maravelakis, P., Psarakis, S. & Vännman, K. (2009). Monitoring capability indices using run rules (ed.). Paper presented at . Journal of Quality in Maintenance Engineering, 15(4), 358-370
Open this publication in new window or tab >>Monitoring capability indices using run rules
2009 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 15, no 4, p. 358-370Article in journal (Refereed) Published
Abstract [en]

PurposeThe purpose of this paper is propose a methodology for monitoring industrial processes that cannot be stabilized, but are nevertheless capable. Design/methodology/approach The proposed procedure uses the C P (u,v) family of capability indices proposed by Vännman (including the indices C PK , C PM , C PMK ) combined with one-sided two-out-of-three and three-out-of-four run rules strategies. FindingsThis paper introduces a new strategy, where capability indices are monitored in place of the classical sample statistics like the mean, median, standard deviation or range. Practical implicationsWhen doing a capability analysis it is recommended to first check that the process is stable, e.g. by using control charts. However, there are occasions when a process cannot be stabilized, but is nevertheless capable. Then the classical control charts fail to efficiently monitor the process position and variability. The approach suggested in this paper overcomes this problem. Originality/valueThe experimental results presented in this paper demonstrate how the new proposed approach efficiently monitors capable processes by detecting decreases or increases of capability level.

National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-7878 (URN)10.1108/13552510910997733 (DOI)64d993f0-ceca-11de-b769-000ea68e967b (Local ID)64d993f0-ceca-11de-b769-000ea68e967b (Archive number)64d993f0-ceca-11de-b769-000ea68e967b (OAI)
Note
Validerad; 2009; 20091111 (ysko)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Albing, M. & Vännman, K. (2009). Skewed zero-bound distributions and process capability indices for upper specifications (ed.). Paper presented at . Journal of Applied Statistics, 36(2), 205-221
Open this publication in new window or tab >>Skewed zero-bound distributions and process capability indices for upper specifications
2009 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 2, p. 205-221Article in journal (Refereed) Published
Abstract [en]

A common practical situation in process capability analysis, which is not well developed theoretically, is when the quality characteristic of interest has a skewed distribution with a long tail towards relatively large values and an upper specification limit only exists. In such situations it is not uncommon that the smallest possible value of the characteristic is 0 and this also is the best value to obtain. Hence a target value 0 is assumed to exist. We investigate a new class of process capability indices for this situation. Two estimators of the proposed index are studied and the asymptotic distributions of these estimators are derived. Furthermore we suggest a decision procedure useful when drawing conclusions about the capability at a given significance level, based on the estimated indices and their asymptotic distributions. A simulation study is also performed, assuming that the quality characteristic is Weibull distributed, to investigate the true significance level when the sample size is finite.

National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics with special emphasis on Industrial Statistic
Identifiers
urn:nbn:se:ltu:diva-12942 (URN)10.1080/02664760802443954 (DOI)000261738300006 ()2-s2.0-57949109145 (Scopus ID)c1716d10-79c4-11dd-b356-000ea68e967b (Local ID)c1716d10-79c4-11dd-b356-000ea68e967b (Archive number)c1716d10-79c4-11dd-b356-000ea68e967b (OAI)
Note
Validerad; 2009; 20080903 (malina)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
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