摘要
武器装备担负保卫国土安全的重要使命,其保持稳定运行状态具有重大国防、政治意义;因其装备运行状态不便中断、故障定位过程复杂,使得传统维修方式效率较低;装备使用数据具有连续性、长期性、不平稳性,甚至一些深度学习模型无法处理其中的退化状态历史依赖与关联问题;通过构建元器件层级的剩余寿命预测架构,对特征工程、退化指标构建以及Transformer-Bi-LSTM模型开展研究,采用距离编码技术,实现针对深度学习模型的技术创新,优化模型预测效果;基于某型武器装备主要器件正常试样数据,进行本方法分析验证,在器件已运行时间达到90%设计试验寿命长度时能够进行有效且准确的剩余寿命预测,所提方法满足武器装备器件寿命预警及更换提醒、保障装备战备完好性的应用需求。
Weaponry is responsible for the important mission of safeguarding national security,and its stable operation is of great nation defense and political significance.Due to the inconvenient interruption of the equipment operation status and the complex fault location process,it results in lower efficiency of traditional maintenance methods.The equipment usage data has the characteristics of continuity,long-term,and instability,and some deep learning models cannot deal with the historical dependence and association of the degraded states.The remaining life prediction architecture at the component level is built to study the feature engineering,degradation index construction and Transformer bi-directional long short-term memory(Bi-LSTM)model.The distance coding is used to realize the technological innovation of the deep learning model and optimize the prediction effect of the model.Based on normal sample data of primary components of certain weapons and equipment,this method analyzes and validates the remaining life of the device,it can be effectively and accurately predicted in operation with 90%of the designed test life.The proposed method meets the application requirements of early warning and replacement for weaponry components,ensuring weaponry readiness.
作者
袁玉昕
程跃兵
熊敏艳
高王升
张昱彤
YUAN Yuxin;CHENG Yuebing;XIONG Minyan;GAO Wangsheng;ZHANG Yutong(Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)
出处
《计算机测量与控制》
2024年第7期203-210,共8页
Computer Measurement &Control
