In the automotive sector, engine testing generates vast data volumes that
are mainly beneficial to requesting engineers. However, these tests are often
not revisited for further analysis due to inconsistent data quality and
a lack of structured assessment methods. Moreover, the absence of a tailored
knowledge discovery process hinders effective preprocessing, transformation,
analytics, and visualization of data, restricting the potential for
historical data insights. Another challenge arises from the heterogeneous
nature of test structures, resulting in varying measurements, data types,
and contextual requirements across different engine test datasets.
This thesis aims to overcome these obstacles by introducing a specialized
knowledge discovery approach for the distinctive Multiple Inconsistent
Heterogeneous Data (MIHData) format characteristic of engine testing.
The proposed methods include adapting data quality assessment and reporting,
classifying engine types through compositional features, employing modified dendrogram similarity measures for classification, performing customized feature extraction, transformation, and structuring, generating and manipulating synthetic images to enhance data visualization, and
applying adapted list-based indexing for multivariate engine test summary
data searches.
The thesis demonstrates how these techniques enable exploratory analysis,
visualization, and classification, presenting a practical framework to
extract meaningful insights from historical data within the engineering
domain. The ultimate objective is to facilitate the reuse of past data resources,
contributing to informed decision-making processes and enhancing
comprehension within the automotive industry. Through its focus on
data quality, heterogeneity, and knowledge discovery, this research establishes
a foundation for optimized utilization of historical Engine Test Data
(ETD) for improved insights. / Soroptimist International Bradford
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19884 |
| Date | January 2022 |
| Creators | Jenkins, Natasha N. |
| Contributors | Not named |
| Publisher | University of Bradford, Department of Computer Science. Faculty of Engineering and Informatics |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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