首页|不确定环境下的航空发动机装配线适应性调度方法

不确定环境下的航空发动机装配线适应性调度方法

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航空发动机装配是航空发动机制造过程的关键环节,其工序多,流程复杂,生产过程中扰动频发,如装配时间波动、不合格返工等。针对不确定环境下的航空发动机装配线的调度问题,提出一种基于门控循环神经网络(GRU)的适应性调度方法。该调度方法包含扰动识别和调度规则调整两个部分:扰动识别模块以滑动时间窗口为周期,利用GRU神经网络进行渐近型扰动的识别;调度规则调整模块以扰动识别的结果为触发,通过构建基于GRU神经网络的调度规则决策模型,输出适配当前生产状态的新的调度规则,用以指导生成更新的调度方案。最后,以某航空发动机装配线为研究案例,对所提出适应性调度方法进行验证分析。对比实验结果表明,所提出方法能够有效提升装配线的设备利用率、日均生产率等性能。
Adaptive scheduling method of aero-engine assembly line in uncertain environment
Aero-engine assembly is the key part of the manufacturing process of aero-engine which are composed of many complex productions.And disturbances appear frequently in the manufacturing process,such as assembly time fluctuation,unqualified rework,etc.Aiming at the scheduling problem of the aero-engine assembly line under uncertain environment,the adaptive scheduling method based on a gate recurrent unit(GRU)neural network is studied.The scheduling method includes two core modules:Disturbance identification and scheduling scheme adjustment.In the disturbance recognition module,a GRU neural network is built to identify progressive disturbances in rolling time windows.Once disturbances are recognized,the scheduling adjustment module will be driven,and the best scheduling rules on current scenarios based on the GRU neural network are output,which are used to update scheduling scheme.Finally,a case study is carried out for an aero-engine assembly line.The comparative experiments show that the proposed method can effectively improve the equipment utilization rate and daily productivity of the assembly line.

adaptive schedulingdisturbance recognitiondeep learninggate recurrent unitaero-engine assembly linedispatching rules

王怡琳、刘鹃、乔非、张家谔

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同济大学电子与信息工程学院,上海 201800

中国航空制造技术研究院,北京 100020

适应性调度 扰动识别 深度学习 门控循环神经网络 航空发动机装配 调度规则

国家自然科学基金重点项目国家自然科学基金面上项目国家自然科学基金面上项目

621330116197323761873191

2024

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子:1.227
ISSN:1001-0920
年,卷(期):2024.39(5)
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