A system reliability evaluation method based on time-series multi-layer complex network

By employing a time-series multilayer complex network evaluation method, the reliability assessment problem of nonlinear interactions and time dependencies in complex product systems is solved. This method enables dynamic tracking of product component failure behavior and identification of key nodes, thereby improving the accuracy of reliability assessment and decision support.

CN120911118BActive Publication Date: 2026-06-19SHANGHAI JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI JIAOTONG UNIV
Filing Date
2025-08-04
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately assess the nonlinear interactions, time dependencies, and complex topologies of product components in complex product systems, leading to inaccurate reliability analyses.

Method used

A system reliability assessment method based on temporal multilayer complex networks is adopted. By dividing maintenance records into spatiotemporal components, constructing single-layer networks, coupling multilayer networks, generating superadjacency matrices, and solving eigencenters, the nonlinear failure coupling effect and dynamic propagation path between product components are quantified. The confidence interval is calculated by combining Monte Carlo simulation.

Benefits of technology

It enables accurate reliability assessment of complex product systems, dynamically tracks failure propagation paths, identifies key nodes, provides statistical basis for risk management, and improves the accuracy of reliability assessment and decision-making flexibility.

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Abstract

This invention relates to a system reliability assessment method based on a temporal multilayer complex network, comprising the following steps: Step 1, spatiotemporal partitioning of maintenance records; Step 2, single-layer network construction; Step 3, multilayer network coupling; Step 4, superadjacency matrix generation; Step 5, feature centrality solution; Step 6, reliability index extraction; Step 7, confidence assessment. This invention innovatively proposes a time-aware multilayer network framework for the reliability prediction and warranty management of complex products. This model overcomes the limitations of traditional reliability analysis, such as simplified system structure and neglect of cascading failures. By constructing a cross-layer temporal analytical network, it integrates the short-term interdependencies of product component degradation and cross-layer cascading failure mechanisms. The developed supercentrality matrix and dynamic node centrality metric can systematically quantify the impact of product components on system reliability and accurately identify key nodes.
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