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

Chunting Liu and Guozhu Jia
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Chunting Liu: School of Economics and Management, Beihang University, Beijing 100191, China
Guozhu Jia: School of Economics and Management, Beihang University, Beijing 100191, China

Sustainability, 2019, vol. 11, issue 17, 1-17

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.

Keywords: industrial big data; computational sustainability; multi-method comparison; reliability and sustainability; high-dimensional data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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