上海交通大学学报

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (3): 322-330.doi: 10.16183/j.cnki.jsjtu.2020.03.012

• 学报（中文） • 上一篇    

面向火箭总装过程的工期延误预警方法

张洁1，赵新明2，张朋2，盛夏2，晁晓娜3，田凤祥3   

  1. 1. 东华大学 机械工程学院， 上海 201620； 2. 上海交通大学 机械与动力工程学院， 上海 200240； 3. 上海航天设备制造总厂有限公司， 上海 200240
  • 出版日期:2020-03-28 发布日期:2020-04-09
  • 通讯作者: 张洁（1963-），女，江苏省江阴市人，教授，博士生导师，主要研究方向为工业大数据智能挖掘分析与决策. 电话（Tel.）:021-67792562; E-mail:mezhangjie@dhu.edu.cn.
  • 基金资助:
    国家自然科学基金 (U1537110, 51435009) 资助项目

Early Warning Method for Tardiness Precaution Oriented to Rocket Final Assembly Process

ZHANG Jie 1,ZHAO Xinming 2,ZHANG Peng 2,SHENG Xia 2,CHAO Xiaona 3,TIAN Fengxiang 3   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China; 2. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Shanghai Aerospace Equipments Manufacture Co., Ltd., Shanghai 200240, China
  • Online:2020-03-28 Published:2020-04-09

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摘要： 为了预防运载火箭总装过程中因动态事件引发的工期延误问题，保证火箭总装任务的按期交付，提出一种工期延误预警方法.该方法包括3个关键步骤：警情监测、警兆识别与警度预报.通过分析火箭总装工期的各种扰动因素及其作用机理，设计定量模型衡量各扰动因素的预警指标，实现火箭总装任务的进度监测.通过充分考虑各警度等级样本数量的不平衡性，应用不平衡分类算法实现警兆识别.通过综合考虑预警样本拖期程度与预警时间节点调整的难易度，设计相应的警度等级以实现警度预报.将该预警方法应用于上海某航天总装厂的实际总装过程数据，以验证该方法的有效性与优越性.

关键词: 预警模型； 工期延误； 火箭总装过程； 不平衡学习

Abstract: To prevent the overdue risks caused by randomness and dynamic events during rocket final assembly process, an early warning method for tardiness precaution is proposed to ensure in-time delivery. The method includes three steps: indicator monitoring, warning sign recognition and warning level prognostication. The inputs are quantized indicators which are elaborately designed by analyzing key factors and their mechanisms influencing the cycle time. Its main task is to monitor the progress of the rocket final assembly. An imbalanced learning algorithm considering the unbalanced nature of the data under different warning levels is utilized to achieve warning sign recognition. By considering both the tardiness of the job and the difficulty in adjustment during sample collecting, the warning level of sample is designed to realize warning level prognostication. The effectiveness and superiority of the proposed model are proven by applying the model to practical production data collected from some aerospace equipment company in Shanghai.

Key words: early waring model; tardiness precaution; rocket final assembly process; imbalanced learning

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