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

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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.

Keywords

Power consistency Causal variables analysis Transitive effects Mutual information Network deconvolution 

Notes

Acknowledgements

This work was supported by financial support of National Science Foundation of China (Nos. 51435009, 51775348), National Technology Support Program of China (No. 2015BAF12B02) and Shanghai Aerospace Science and Technology Innovation Fund (No. SAST2016048).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.College of Mechanical EngineeringDonghua UniversityShanghaiChina

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