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Journal of Intelligent Manufacturing

Erschienen in:

14.02.2018

An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution

verfasst von: Wei Qin, Dongye Zha, Jie Zhang

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 7/2020

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Abstract

The effective control of the power consistency, which is one of the most important quality indicators of diesel engine, plays a decisive role for improving the competitiveness of the products. The widely used sensors and other data acquisition equipment make the “data-driven quality control” become possible. However, how to determine the highly related parameters with the engine power from massive captured manufacturing data and effectively discriminated the direct and indirect dependencies between these variables are still challenging. This paper proposed a feature selection algorithm named NMI-ND which uses network deconvolution (ND) to infer causal correlations among various diesel engine manufacturing parameters from the observed correlations based on normalized mutual information (NMI). The proposed algorithm is thoroughly evaluated through the experimental study by comparing it with other representative feature selection algorithms. The comparison demonstrates that NMI-ND performs better in both effectiveness and efficiency.

Vorheriger Artikel Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization

Nächster Artikel A scheduling optimization method for maintenance, repair and operations service resources of complex products

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Titel: An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution
verfasst von: Wei Qin
Dongye Zha
Jie Zhang
Publikationsdatum: 14.02.2018
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 7/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-018-1397-8

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