A real time methodology of cluster-system theory-based reliability estimation using k-means clustering. (October 2020)

• Record Type: Journal Article
• Authors: Cai, Wei; Zhao, Jingyi; Zhu, Ming
• Is Part Of: Reliability engineering & system safety. Volume 202(2020)
• Publication Date: 2020-10
• Subjects: cluster-system theory -- real time reliability estimation methodology -- k-means clustering -- family-system -- complex system engineering; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.107045
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Basic theory of cluster-system, the special systems in complex systems The division of family-systems, thus the sub-system samples can be expanded Real-time analysis of key performance trends of sub-systems to obtain reliability estimation results Application in the Five-hundred-meter Aperture Spherical Telescope (FAST) demonstrates the effectiveness of the proposed method Abstract: With rapidly increase in the design and application of complex system engineering, life and reliability analysis methods for these engineering have received much attention. A novel real time reliability analysis methodology is proposed based on the cluster-system theory and k-means clustering. Firstly, the system consisting of three or more identical or similar sub-systems facing the same load or task is defined as cluster-system. By analyzing the key performance parameters, sub-systems with similar performance are divided into a family-system, so that each sub-system can be used as reference sample for other sub-systems in the identical family-system. The cubic spline interpolation method under first-order boundary conditions is used to fit the average variation of key performance parameters of family-systems. The residual life of family-system can be obtained by determining the threshold of key performance parameters of sub-system through experiments or experience. Then reliability of the whole system is estimated by the contribution of each sub-system in cluster-system to solve the problem that there is no fault data and reference samples in the reliability analysis of complex system. Application in the Five-hundred-meter Aperture Spherical Telescope (FAST) demonstrates the effectiveness of the proposed method, compared to traditional reliability analysis methods based on experimental statistics.