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Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Author

Listed:
  • Chunting Liu

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Guozhu Jia

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.

Suggested Citation

  • Chunting Liu & Guozhu Jia, 2019. "Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4557-:d:259820
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    References listed on IDEAS

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    1. Lei Wang & Qingjian Zhao & Zuomin Wen & Jiaming Qu, 2018. "RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    2. Kyu Jong Lee & Hyungu Kahng & Seoung Bum Kim & Sun Kyoung Park, 2018. "Improving Environmental Sustainability by Characterizing Spatial and Temporal Concentrations of Ozone," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
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    Cited by:

    1. Baiyun Qian & Jinjun Huang & Xiaoxun Zhu & Ruijun Wang & Xiang Lin & Ning Gao & Wei Li & Lijiang Dong & Wei Liu, 2022. "Research on the Fault Diagnosis Method of a Synchronous Condenser Based on the Multi-Scale Zooming Learning Framework," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

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