一种数据驱动的不可靠装配流水线性能评估方法
By employing a data-driven assembly line performance evaluation method, which utilizes multi-source heterogeneous data and generative adversarial networks to generate equipment health status data, the problem of insufficient data utilization in assembly line management is solved, enabling efficient performance evaluation and scientific decision-making.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2023-04-25
- Publication Date
- 2026-07-17
AI Technical Summary
Existing assembly line management relies on manual experience and fails to make full use of multi-source heterogeneous data, resulting in difficulties in quantifying the impact of disturbances in the production workshop, characterizing the state space, evaluating system performance, and making decisions lack foresight and scientific rigor.
This paper proposes a data-driven performance evaluation method for unreliable assembly lines. By collecting heterogeneous data from multiple sources, a data generation model for equipment health status and an operational logic model are constructed. Probability density fitting and generative adversarial networks are used to generate equipment health status data, and simulation experiments are conducted to evaluate performance indicators.
It enables data-driven performance evaluation of assembly lines, improves production efficiency and scientific decision-making, reduces production costs, and provides a reliable performance evaluation tool.
Smart Images
Figure CN116596289B_ABST