.png){kind=icon}
返回
High-Dimensional Data Global Sensitivity Analysis Based on Deep Soft Sensor Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2022.3169637.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article investigates the sensitivity analysis (SA) of high-dimensional data to identify the effects of process variables on output quantity of interest (QoI) in industrial soft sensor modeling. The computational cost of analyzing the SA of high-dimensional data is high, and models available for SA techniques usually have limited generalization capacity. Therefore, we propose a novel high-dimensional data global SA (GSA) approach based on a deep soft sensor model to address these issues. We first develop an approximately incremental grouping (AIG) algorithm and a region-based cooperative co-evolution (RBCC) algorithm to decompose the high-dimensional data into independent regions for the GSA. Subsequently, a multihead deep soft sensor model with generalization performance is designed to determine the GSA indices of each decomposed region. Specifically, the region of interest (RoI) align algorithm provides the multihead with precisely located decomposed region features. Finally, based on the uncertainty analysis of each model head, we present a joint loss function with the Monte Carlo dropout (MC-dropout) algorithm to measure the GSA indices of each decomposed region on QoIs. Experimental evaluation results on a benchmark dataset and a real-world one demonstrate the effectiveness of the proposed approach in addressing the GSA of high-dimensional data in industrial processes.
Keyword:
Soft sensors
Data models
Analytical models
Uncertainty
Numerical models
Industries
Computational modeling
Cooperative co-evolution
deep soft sensor
global sensitivity analysis (GSA)
high-dimensional data
uncertainty analysis
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks基于多传感器和卷积神经网络的旋转机械故障诊断
PREAI
分享收藏
Variable chromosome length genetic algorithm for progressive refinement in topology optimization
PREAI
分享收藏Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization面向大规模优化的差分分组协同进化算法
OAAI
OAAI分享收藏
查看更多内容