面向高维度小样本场景的船用柴油机装配质量评估
Assembly quality assessment of marine diesel engines for high-dimensional and small-sample scenarios
冯麟皓 1王叶松 1方喜峰 1于航 1李群2
作者信息
- 1. 江苏科技大学机械工程学院,江苏 镇江 212003
- 2. 上海沪东重机有限公司,上海 200120
- 折叠
摘要
针对现阶段船用柴油机年均产量较小、质量数据不足、无法及时对装配质量进行准确评估的问题,文章提出了一种面向高维度小样本的质量评估方法.针对数据不平衡,提出了一种VAE-GAN的数据生成方法,使用VAE网络增强了数据编码过程,有效扩充了原始数据;并构建了特征筛选网络剔除"冗余特征",提取关键工序以提高训练效果;最后基于CNN-LSTM网络对装配过程进行时序建模,提高了装配质量评估的准确性.并使用某船用柴油机的部装质量数据进行实验验证,为高维度小样本数据的质量评估提供了理论参考.
Abstract
In response to the current issue of low annual production of marine diesel engines,resulting in insufficient quality data and an inability to accurately assess assembly quality in a timely manner,a quality assessment method for high-dimensional small samples has been proposed.In consideration of data imbalance,a data generation approach based on VAE-GAN has been introduced,wherein the coding process is enhanced using VAE networks,and the original dataset is effectively expanded.Furthermore,a feature selection network has been constructed to eliminate redundant features and extract key processes,thereby improving the effectiveness of training.Finally,the assembly process has been modeled as time series data by CNN-LSTM networks,enhancing the accuracy of assembly quality evaluation.Experimental verification has been conducted using quality data from a marine diesel engine,providing theoretical guidance for evaluating high-dimensional small sample datasets.
关键词
船用柴油机/装配质量评估/高维度小样本/特征筛选/深度学习Key words
marine diesel engine/assembly quality assessment/high dimensional small sample/feature screening/deep learning引用本文复制引用
基金项目
国防基础科研基金项目(A0720133010)
镇江市重点研发计划项目(GY2020007)
江苏科技大学2023年度研究生教育教学改革研究课题项目(YJG2023Z_01)
出版年
2024
NETL
NSTL
万方数据