Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction-Reference-Cited by-同舟云学术

Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction

Published:2018-04-01 Issue:4 Volume:66 Page:344-355
ISSN:2196-677X
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

Weiß Iris¹,Vogel-Heuser Birgit¹

Affiliation:

1. Technical University of Munich , Institute of Automation and Information Systems , Boltzmannstr. 15 , 85748 Garching near Munich , Germany

Abstract

Abstract Data mining in automated production systems provide high potential to increase the Overall Equipment Effectiveness. Nevertheless, data of such machines/plants include specific characteristics regarding the variance and distribution of the dataset. For modelling product quality prediction, these characteristics have to be analysed to interpret the results correctly. Therefore, an approach for the analysis of variance and distribution of datasets is proposed. The evaluation of this approach validates the developed guidelines, which identify the reasons for inconsistent prediction results based on two different datasets of the same production system.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://www.degruyter.com/document/doi/10.1515/auto-2017-0115/pdf

Reference20 articles.

1. B. Vogel-Heuser and D. Hess. 2016. Guest Editorial Industry 4.0–Prerequisites and Visions. IEEE Trans. Automat. Sci. Eng., pp. 411–413.

2. B. Vogel-Heuser, V. Karaseva, J. Folmer, and I. Kirchen. 2017. Operator Knowledge Inclusion in Data-Mining Approaches for Product Quality Assurance using Cause-Effect Graphs. In: 20th IFAC World Congress (IFAC), pp. 1358–1365.

3. I. Kirchen, D. Schütz, J. Folmer, and B. Vogel-Heuser. 2017. Metrics for the Evaluation of Data Quality of Signal Data in Industrial Processes. In: 15th IEEE International Conference on Industrial Informatics (INDIN).

4. D. Pantförder, J. Schaupp, and B. Vogel-Heuser. 2017. Making Implicit Knowledge Explicit – Acquisition of Plant Staff’s Mental Models as a Basis for Developing a Decision Support System. In: 19th International Conference on Human-Computer Interaction, pp. 358–365.

5. Z. Ge. 2014. Quality prediction and analysis for large-scale processes based on multi-level principal component modeling strategy. Control Engineering Practice, vol. 31, pp. 9–23.10.1016/j.conengprac.2014.06.006

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on full-process product defect traceability analysis technology based on workshop big data;2022 2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI);2022-10-21

2. Datenqualität in CPPS;Springer Reference Technik;2020