摘要
提出了基于性能时变数据分析的再设计模块识别方法.利用产品在健康状态下的性能时变数据构建无监督学习的稀疏自编码神经网络(SAENN)模型,以用于健康状态下产品性能数据的特征提取以及产品功能退化程度的评估;将产品在健康状态下的性能数据用于训练SAENN模型,使用运行期间的性能时变数据更新产品的状态特征,以反映功能的退化过程;通过对比功能间的退化差异来识别需要再设计模块;同时,以某制造企业水平定向钻产品再设计功能模块的识别为例验证了所提方法的可行性.结果表明,所提出的再设计模块识别方法具有较好的准确性,能够识别需改进的功能模块,识别结果可作为产品再设计的依据.
This paper presents a redesign-module identification method based on time-varying product usage data. The method is conducted in three steps:Building a model based on sparse auto-encoder (SAE) neural network using performance feature data from the product health state;Assessing functional performance degradation by using the data during the actual operation;Identifying the redesign module by comparing the difference in functional degradation. Then, a case study of horizontal directional drilling redesign module identification is presented to illustrate feasibility of the proposed method. The result shows the effectiveness of proposed method so that it can identify the weak function modules, while the identified result can provide support for redesign decision-making.
作者
马斌彬
马红占
褚学宁
李玉鹏
MA Binbin;MA Hongzhan;CHU Xuening;LI Yufieng(School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2019年第7期838-843,共6页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金项目(51875345,51475290,51075261,51505480)
关键词
产品再设计
模块识别
性能时变数据
稀疏自编码神经网络
功能退化
product redesign
module identification
time-varying performance feature data
sparse autoencoder neural network (SAENN)
function degradation
