Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control

Selvamuthu, Dharmaraja; Das, Dipayan

doi:10.1007/978-981-13-1736-1

Cited by 63 publications

(57 citation statements)

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“…In the sense of design of experiments (DOE), the test series A and test series B vary in the applied test design, i.e., the factor plan. For the basics of DOE and test designs in general, the authors refer to the corresponding relevant literature [39,43]. The two test designs differ in the number of test points and also in the number of investigated factor levels (Section 3.1).…”

Section: Methods and Experimental Set-upmentioning

confidence: 99%

“…When planning, implementing, and evaluating a sensitivity analysis, it must also be taken into account that in addition to the stress factors under investigation, disturbance factors can also affect the aging process and thus the test results of the sensitivity analysis ( Figure 1d). Disturbance factors, thus factors whose influence on aging is not of interest, must be brought under experiment control in order to keep the error dispersion low [38,39]. It is the aim of a robust test design to hold the disturbance factors and thus their influence on the results negligible or at least constant [39].…”

Section: Consideration Of Disturbance Factorsmentioning

confidence: 99%

“…Disturbance factors, thus factors whose influence on aging is not of interest, must be brought under experiment control in order to keep the error dispersion low [38,39]. It is the aim of a robust test design to hold the disturbance factors and thus their influence on the results negligible or at least constant [39]. In Table 3, the relevant disturbance factors for calendar and cyclic aging tests-investigating one specific cell type-are listed.…”

Section: Consideration Of Disturbance Factorsmentioning

confidence: 99%

“…This is at the expense of blending individual and interaction effects for the various stress factors, but shows the essential advantage that the number of test points, and thus testing costs, are reduced, especially if a greater number of factor levels is desired. For the creation of fractional factorial designs, different well-established design guidelines are available through which valid statements can be made for the entire, though partially occupied, experimental space [39]. Especially in the case of lithium-ion cell testing, where the combination of factor levels is subjected to practical constraints, a D-optimal fractional factorial test design is a good choice.…”

Section: Test Designsmentioning

confidence: 99%

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Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

et al. 2020

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As storage technology in electric vehicles, lithium-ion cells are subject to a continuous aging process during their service life that, in the worst case, can lead to a premature system failure. Battery manufacturers thus have an interest in the aging prediction during the early design phase, for which semi-empirical aging models are often used. The progress of aging is dependent on the application-specific load profile, more precisely on the aging-relevant stress factors. Still, a literature review reveals a controversy on the aging-relevant stress factors to use as input parameters for the simulation models. It shows that, at present, a systematic and efficient procedure for stress factor selection is missing, as the aging characteristic is cell-specific. In this study, an accelerated sensitivity analysis as a prior step to aging modeling is proposed, which is transferable and allows to determine the actual aging-relevant stress factors for a specific lithium-ion cell. For the assessment of this accelerated approach, two test series with different acceleration levels and cell types are performed and evaluated. The results show that a certain amount of charge throughput, 100 equivalent full cycles in this case, is necessary to conduct a statistically significant sensitivity analysis.

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Section: Methods and Experimental Set-upmentioning

confidence: 99%

Section: Consideration Of Disturbance Factorsmentioning

confidence: 99%

Section: Consideration Of Disturbance Factorsmentioning

confidence: 99%

Section: Test Designsmentioning

confidence: 99%

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Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

et al. 2020

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“…Multifactor experimental designs, [5,16] are also alternatives to estimate the mean differences but also rely on homoscedasticity. They are mostly used in the context of statistically planned experiments, which consists of a few experimental runs to obtain data on the product characteristics.…”

Section: Previous Workmentioning

confidence: 99%

Quality Analysis in Acyclic Production Networks

Gutiérrez

Müller

2019

Stochastics and Quality Control

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The production network under examination consists of a number of workstations. Each workstation is a parallel configuration of machines performing the same kind of tasks on a given part. Parts move from one workstation to another and at each workstation a part is assigned randomly to a machine. We assume that the production network is acyclic, that is, a part does not return to a workstation where it previously received service. Furthermore, we assume that the quality of the end product is additive, that is, the sum of the quality contributions of the machines along the production path. The contribution of each machine is modeled by a separate random variable.Our main result is the construction of estimators that allow pairwise and multiple comparison of the means and variances of machines in the same workstation. These comparisons then may lead to the identification of unreliable machines. We also discuss the asymptotic distributions of the estimators that allow the use of standard statistical tests and decision making.

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Academic performance of engineering students: A predictive validity study of first‐year GPA and final‐year CGPA

Nurudeen,

Fakhrou,

Lawal

et al. 2023

Engineering Reports

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Students' academic achievement is regarded as the scholastic standing of students at the end of a given study period that is expressed in terms of grades. This study focused on students' first‐year GPA as a predictor of final‐year CGPA and the relationship between demographic attributes of the students to academic achievement. An ex‐post factor research approach was adopted, and Pearson's correlation and Regression Analysis were fitted with the data using Minitab software. The results of the study highlighted that first‐year GPA had a strong positive relationship with final‐year CGPA which is an indication that first‐year GPA greatly influences final‐year CGPA meanwhile, demographic characteristics have no significant correlation with students' final‐year CGPA. Finally, the correlations drawn from this study indicated that first‐year GPA had a strong positive relationship with final‐year CGPA. Therefore, the students should be properly guided, University administrators, Faculty members, policy‐makers and other stakeholders should effectively develop a monitoring blueprint for students' academic progress as poor performance has severe consequences on students, teachers should inculcate effective learning culture among students, while government and its supervisory agencies should provide adequate teaching and learning resources, create enabling learning environment for effective service delivery that focuses on improving students' performance in Engineering.

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Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control

Cited by 63 publications

References 0 publications

Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging

Quality Analysis in Acyclic Production Networks

Academic performance of engineering students: A predictive validity study of first‐year GPA and final‐year CGPA

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