Random Forest-Bayesian Optimization for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber-Physical Systems
Random Forest-Bayesian Optimization for Product Quality Prediction With Large-Scale Dimensions in Process Industrial Cyber-Physical Systems
复制标题
过程工业信息物理系统中大尺寸产品质量预测的随机森林贝叶斯优化
DOI：
10.1109/jiot.2020.2992811
复制
发表时间：
2020-09-01
影响因子：
10.6
通讯作者：
Ruan, Junhu
中科院分区：
文献类型：
--
作者：
Wang, Tianteng;Wang, Xuping;Ruan, Junhu
网络物理系统和数据驱动技术具有促进产品质量预测和控制的潜力，这是现代工业中最重要的两个问题之一。本文将随机森林与贝叶斯优化相结合，利用大规模维度数据进行质量预测，通过信息增益选择关键生产要素，然后利用灵敏度分析来维持产品质量。通过横向实验，验证了贝叶斯优化中嵌入的RF比经典RF、支持向量机、Logistic回归、决策树，甚至背景传播神经网络的优势。此外，我们发现，RF-Bayes优化处理的关键特征较少，可以实现令人满意的预测精度和成本效益计算时间，我们用赫伯特·A·西蒙的管理决策理论和帕累托原理对其进行了解释。因此，研究结果可以为实际流程工业的产品质量预测和控制提供管理见解和操作指导。
Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this article, we integrate random forest (RF) with Bayesian optimization for quality prediction with large-scale dimensions data, selecting crucial production elements by information gain, and then utilizing sensitivity analysis to maintain product quality. Horizontal empirical experiments are performed to verify the superiorities of RF embedded within Bayesian optimization over classical RF, support vector machine, logistic regression, decision tree, and even background propagation neural network. Besides, we find fewer but critical features handled by RF-Bayesian optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon's management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at the real-life process industry.