| 1. |
- Bergquist, Bjarne, et al.
(författare)
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A Bayesian analysis of unreplicated two-level factorials using effects sparsity, hierarchy, and heredity
- 2011
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Ingår i: Quality Engineering. - : Informa UK Limited. - 0898-2112 .- 1532-4222. ; 23:2, s. 152-166
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.
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| 2. |
- Bergquist, Bjarne, et al.
(författare)
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Alive and kicking–but will Quality Management be around tomorrow? : A Swedish academia perspective
- 2012
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Konferensbidrag (refereegranskat)abstract
- Purpose: There is a lack of a recognized conception of quality management (QM) comprises of, as well as a clear roadmap of where QM is heading. The purpose of this article is to investigate how QM is perceived today by scholars at three Swedish universities, but also how and into what QM is expected to develop into in twenty years.Methodology: Data have been collected through three structured workshops using affinity diagrams with scholars teaching and performing research in the QM field affiliated with three different Swedish universities.Findings: The results indicate that current QM is perceived similarly among the universities today, although the taxonomy differs slightly. QM is described as a fairly wide discipline consisting of a set of core of principles that in turn guide which methods and tools that currently by many are perceived as the core of the discipline. The outlook for the future differs more where three possible development directions for QM are seen: [1] searching for a “discipline X” where QM can contribute while keeping its toolbox, [2] focus on a core based on the traditional quality technology toolbox with methods and tools, and [3] a risk that QM, as it is today, may seize to exist and be diffused into other disciplines. Originality/value: This article contributes with a viewpoint on QM today and its future development from the academicians’ perspective.
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| 3. |
- Bergquist, Bjarne, et al.
(författare)
-
Alive and kicking–but will Quality Management be around tomorrow? A Swedish academia perspective
- 2012
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Ingår i: Quality Innovation Prosperity. - : Technical University of Kosice, Faculty of Materials, Metallurgy and Recycling. - 1335-1745 .- 1338-984X. ; 16:2, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of this article is to describe how Quality Management (QM) is perceived today by scholars at three Swedish universities, and into what QM is expected to develop into in twenty years. Data were collected through structured workshops using affinity diagrams with scholars teaching and performing research in the QM field. The results show that QM currently is perceived as consisting of a set of core of principles, methods and tools. The future outlook includes three possible development directions for QM are seen: [1] searching for a “discipline X” where QM can contribute while keeping its toolbox, [2] focus on a core based on the traditional quality technology toolbox with methods and tools, and [3] a risk that QM, as it is today, may seize to exist and be diffused into other disciplines.
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| 4. |
- Bergquist, Bjarne, et al.
(författare)
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Cleaning of Railway Track Measurement Data forBetter Maintenance Decisions
- 2019
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Ingår i: Proceedings of the 5<sup>th</sup> International Workshop and Congress on eMaintenance. - : Luleå University of Technology. ; , s. 9-15
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Konferensbidrag (refereegranskat)abstract
- Data of sufficient quality, quantity and validity constitute a sometimes overlooked basis for eMaintenance. Missing data, heterogeneous data types, calibration problems, or non-standard distributions are common issues of operation and maintenance data. Railway track geometry data used for maintenance planning exhibit all the above issues. They also have unique features stemming from their collection by measurement cars running along the railway network. As the track is a linear asset, measured geometry data need to be precisely located to be useful. However, since the sensors on the measurement car are moving along the track, the observations’ geographical sampling positions come with uncertainty. Another issue is that different seasons and othertime restrictions (e.g. related to the timetable) prohibit regular sampling. Hence, prognostics related to remaining useful life (RUL) are challenging since most forecasting methods require a fixed sampling frequency.This paper discusses methods for data cleaning, data condensation and data extraction from large datasets collected by measurement cars. We discuss missing data replacement, dealing with autocorrelation or cross-correlation, and consequences of not fulfilling methodological pre-conditions such as estimating probabilities of failures using data that do not follow the assumed distributions or data that are dependent. We also discuss outlier detection, dealing with data coming from multiple distributions, of unknown calibrations and other issues seen in railway track geometry data. We also discuss the consequences of not addressing or mishandling quality issues of such data.
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| 5. |
- Bergquist, Bjarne, et al.
(författare)
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In-situ measurement in the iron ore pellet distribution chain using active RFID technology
- 2020
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Ingår i: Powder Technology. - : Elsevier. - 0032-5910 .- 1873-328X. ; 361, s. 791-802
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Tidskriftsartikel (refereegranskat)abstract
- The active radio frequency identification (RFID) technique is used for in-situ measurement of acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based on two experiments, in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to process and product quality improvement, to better understand the transportation process, and to reduce environmental impacts due to dusting.
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| 6. |
- Bergquist, Bjarne, et al.
(författare)
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Power Analysis of Methods for Analysing Unreplicated Factorial Experiments
- 2013
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Konferensbidrag (refereegranskat)abstract
- Several methods for formal analysis of unreplicated factorial type experiments have been proposed in the literature. Based on a simulation study, five formal methods found in the literature based on the effect sparsity principle have been studied. The simulation included 23 and 24 type factorials with one, two, or four active effects. The simulated signal-to-noise ratios for the effects were all between two and four, and the Type I and Type II errors of the analysis methods were analysed. Preliminary results show that Bayesian models are more powerful in these contexts, especially if informative priors based on the effect heredity and effect hierarchy principles are used.
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| 7. |
- Capaci, Francesca, et al.
(författare)
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A two-step procedure for fault detection in the Tennessee Eastman Process simulator
- 2016
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Konferensbidrag (refereegranskat)abstract
- High-technological and complex production processes and high availability and sample frequencies of data in large scale industrial processes need the concurrent development of appropriate statistical control tools and monitoring techniques. Therefore, multivariate control charts based on latent variables are essential tools to detect and isolate process faults.Several Statistical Process Control (SPC) charts have been developed for multivariate and megavariate data, such as the Hotelling T2, MCUSUM and MEWMA control charts as well as charts based on principal component analysis (PCA) and dynamic PCA (DPCA). The ability of SPC procedures based on PCA (Kourti, MacGregor 1995) or DPCA (Ku et al. 1995) to detect and isolate process disturbances for a large number of highly correlated (and time-dependent in the case of DPCA) variables has been demonstrated in the literature. However, we argue that the fault isolation capability and the fault detection rate for processes can be improved further for processes operating under feedback control loops (in closed loop).The purpose of this presentation is to illustrate a two-step method where [1] the variables are pre-classified prior to the analysis and [2] the monitoring scheme based on latent variables is implemented. Step 1 involves a structured qualitative classification of the variables to guide the choice of which variables to monitor in Step 2. We argue that the proposed method will be useful for many practitioners of SPC based on latent variables techniques in processes operating in closed loop. It will allow clearer fault isolation and detection and an easier implementation of corrective actions. A case study based on the data available from the Tennessee Eastman Process simulator under feedback control loops (Matlab) will be presented. The results from the proposed method are compared with currently available methods through simulations in R statistics software.
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| 8. |
- Capaci, Francesca
(författare)
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Adapting Experimental and Monitoring Methods for Continuous Processes under Feedback Control : Challenges, Examples, and Tools
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Continuous production covers a significant part of today’s industrial manufacturing. Consumer goods purchased on a frequent basis, such as food, drugs, and cosmetics, and capital goods such as iron, chemicals, oil, and ore come through continuous processes. Statistical process control (SPC) and design of experiments (DoE) play important roles as quality control and product and process improvement methods. SPC reduces product and process variation by eliminating assignable causes, while DoE shows how products and processes may be improved through systematic experimentation and analysis. Special issues emerge when applying these methods to continuous process settings, such as the need to simultaneously analyze massive time series of autocorrelated and cross-correlated data. Another important characteristic of most continuous processes is that they operate under engineering process control (EPC), as in the case of feedback controllers. Feedback controllers transform processes into closed-loop systems and thereby increase the process and analysis complexity and application of SPC and DoE methods that need to be adapted accordingly. For example, the quality characteristics or process variables to be monitored in a control chart or the experimental factors in an experiment need to be chosen considering the presence of feedback controllers.The main objective of this thesis is to suggest adapted strategies for applying experimental and monitoring methods (namely, DoE and SPC) to continuous processes under feedback control. Specifically, this research aims to [1] identify, explore, and describe the potential challenges when applying SPC and DoE to continuous processes; [2] propose and illustrate new or adapted SPC and DoE methods to address some of the issues raised by the presence of feedback controllers; and [3] suggest potential simulation tools that may be instrumental in SPC and DoE methods development.The results are summarized in five appended papers. Through a literature review, Paper A outlines the SPC and DoE implementation challenges for managers, researchers, and practitioners. For example, the problems due to process transitions, the multivariate nature of data, serial correlation, and the presence of EPC are discussed. Paper B describes the issues and potential strategies in designing and analyzing experiments on processes operating under closed- loop control. Two simulated examples in the Tennessee Eastman (TE) process simulator show the benefits of using DoE methods to improve these industrial processes. Paper C provides guidelines on how to use the revised TE process simulator under a decentralized control strategy as a testbed for SPC and DoE methods development in continuous processes. Papers D and E discuss the concurrent use of SPC in processes under feedback control. Paper D further illustrates how step and ramp disturbances manifest themselves in single-input single-output processes controlled by variations in the proportional-integral-derivative control and discusses the implications for process monitoring. Paper E describes a two-step monitoring procedure for multivariate processes and explains the process and controller performance when out-of-controlprocess conditions occur.
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| 9. |
- Capaci, Francesca
(författare)
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Contributions to the Use of Statistical Methods for Improving Continuous Production
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Complexity of production processes, high computing capabilities, and massive datasets characterize today’s manufacturing environments, such as those of continuous andbatch production industries. Continuous production has spread gradually acrossdifferent industries, covering a significant part of today’s production. Commonconsumer goods such as food, drugs, and cosmetics, and industrial goods such as iron,chemicals, oil, and ore come from continuous processes. To stay competitive intoday’s market requires constant process improvements in terms of both effectivenessand efficiency. Statistical process control (SPC) and design of experiments (DoE)techniques can play an important role in this improvement strategy. SPC attempts toreduce process variation by eliminating assignable causes, while DoE is used toimprove products and processes by systematic experimentation and analysis. However,special issues emerge when applying these methods in continuous process settings.Highly automated and computerized processes provide an exorbitant amount ofserially dependent and cross-correlated data, which may be difficult to analyzesimultaneously. Time series data, transition times, and closed-loop operation areexamples of additional challenges that the analyst faces.The overall objective of this thesis is to contribute to using of statisticalmethods, namely SPC and DoE methods, to improve continuous production.Specifically, this research serves two aims: [1] to explore, identify, and outlinepotential challenges when applying SPC and DoE in continuous processes, and [2] topropose simulation tools and new or adapted methods to overcome the identifiedchallenges.The results are summarized in three appended papers. Through a literaturereview, Paper A outlines SPC and DoE implementation challenges for managers,researchers, and practitioners. For example, problems due to process transitions, themultivariate nature of data, serial correlation, and the presence of engineering processcontrol (EPC) are discussed. Paper B further explores one of the DoE challengesidentified in Paper A. Specifically, Paper B describes issues and potential strategieswhen designing and analyzing experiments in processes operating under closed-loopcontrol. Two simulated examples in the Tennessee Eastman (TE) process simulatorshow the benefits of using DoE techniques to improve and optimize such industrialprocesses. Finally, Paper C provides guidelines, using flow charts, on how to use thecontinuous process simulator, “The revised TE process simulator,” run with adecentralized control strategy as a test bed for developing SPC and DoE methods incontinuous processes. Simulated SPC and DoE examples are also discussed.
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| 10. |
- Capaci, Francesca, et al.
(författare)
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Exploring the Use of Design of Experiments in Industrial Processes Operating Under Closed-Loop Control
- 2017
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Ingår i: Quality and Reliability Engineering International. - : John Wiley & Sons. - 0748-8017 .- 1099-1638. ; 33:7, s. 1601-1614
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Tidskriftsartikel (refereegranskat)abstract
- Industrial manufacturing processes often operate under closed-loop control, where automation aims to keep important process variables at their set-points. In process industries such as pulp, paper, chemical and steel plants, it is often hard to find production processes operating in open loop. Instead, closed-loop control systems will actively attempt to minimize the impact of process disturbances. However, we argue that an implicit assumption in most experimental investigations is that the studied system is open loop, allowing the experimental factors to freely affect the important system responses. This scenario is typically not found in process industries. The purpose of this article is therefore to explore issues of experimental design and analysis in processes operating under closed-loop control and to illustrate how Design of Experiments can help in improving and optimizing such processes. The Tennessee Eastman challenge process simulator is used as a test-bed to highlight two experimental scenarios. The first scenario explores the impact of experimental factors that may be considered as disturbances in the closed-loop system. The second scenario exemplifies a screening design using the set-points of controllers as experimental factors. We provide examples of how to analyze the two scenarios
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