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An Engineering Domain Knowledge-Based Framework for Modelling Highly Incomplete Industrial Data

An Engineering Domain Knowledge-Based Framework for Modelling Highly Incomplete Industrial Data

Han Li, Zhao Liu, Ping Zhu
Copyright: © 2021 |Volume: 17 |Issue: 4 |Pages: 19
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799859512|DOI: 10.4018/IJDWM.2021100103
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MLA

Li, Han, et al. "An Engineering Domain Knowledge-Based Framework for Modelling Highly Incomplete Industrial Data." IJDWM vol.17, no.4 2021: pp.48-66. http://doi.org/10.4018/IJDWM.2021100103

APA

Li, H., Liu, Z., & Zhu, P. (2021). An Engineering Domain Knowledge-Based Framework for Modelling Highly Incomplete Industrial Data. International Journal of Data Warehousing and Mining (IJDWM), 17(4), 48-66. http://doi.org/10.4018/IJDWM.2021100103

Chicago

Li, Han, Zhao Liu, and Ping Zhu. "An Engineering Domain Knowledge-Based Framework for Modelling Highly Incomplete Industrial Data," International Journal of Data Warehousing and Mining (IJDWM) 17, no.4: 48-66. http://doi.org/10.4018/IJDWM.2021100103

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Abstract

The missing values in industrial data restrict the applications. Although this incomplete data contains enough information for engineers to support subsequent development, there are still too many missing values for algorithms to establish precise models. This is because the engineering domain knowledge is not considered, and valuable information is not fully captured. Therefore, this article proposes an engineering domain knowledge-based framework for modelling incomplete industrial data. The raw datasets are partitioned and processed at different scales. Firstly, the hierarchical features are combined to decrease the missing ratio. In order to fill the missing values in special data, which is identified for classifying the samples, samples with only part of the features presented are fully utilized instead of being removed to establish local imputation model. Then samples are divided into different groups to transfer the information. A series of industrial data is analyzed for verifying the feasibility of the proposed method.

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