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

Author

Listed:
  • Wei Qin

    (Shanghai Jiao Tong University)

  • Dongye Zha

    (Shanghai Jiao Tong University)

  • Jie Zhang

    (Donghua University)

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.

Suggested Citation

  • Wei Qin & Dongye Zha & Jie Zhang, 2020. "An effective approach for causal variables analysis in diesel engine production by using mutual information and network deconvolution," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1661-1671, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1397-8
    DOI: 10.1007/s10845-018-1397-8
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    References listed on IDEAS

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    1. Junliang Wang & Jie Zhang, 2016. "Big data analytics for forecasting cycle time in semiconductor wafer fabrication system," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7231-7244, December.
    2. Shujie Liu & Yawei Hu & Chao Li & Huitian Lu & Hongchao Zhang, 2017. "Machinery condition prediction based on wavelet and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 1045-1055, April.
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    Cited by:

    1. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.

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