This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart ...This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.展开更多
To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new lig...To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.展开更多
This paper presents an effective decision making framework for software selection in manufacturing industries using a multiple criteria decision making method, Preference Ranking Organization Method for Enrichment Eva...This paper presents an effective decision making framework for software selection in manufacturing industries using a multiple criteria decision making method, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The method is improved in the present work by integrating with analytic hierarchy process (AHP) and the fuzzy logic. Fuzzy logic is introduced to handle the imprecision of the human decision making process. The proposed decision making framework is practical for ranking competing software products in terms of their overall performance with respect to multiple criteria. An example is included to illustrate the approach.展开更多
随着互联网和广告平台的飞速发展,面对海量的广告信息,为了提升用户点击率,提出一种改进的基于组合结构的逻辑回归点击预测算法LRCS(Logical Regression of Combination Structure)。该算法基于不同类别特征广告受众可能不同的特点,首先...随着互联网和广告平台的飞速发展,面对海量的广告信息,为了提升用户点击率,提出一种改进的基于组合结构的逻辑回归点击预测算法LRCS(Logical Regression of Combination Structure)。该算法基于不同类别特征广告受众可能不同的特点,首先,采用FM进行特征组合,产生两类组合特征;其次,将一类特征组合作为聚类算法的输入进行聚类;最后,将另一类特征组合输入由聚类产生的分段GBDT+逻辑回归组合的模型中进行预测。在两个公开数据集中进行了多角度验证,结果表明与其他几类常用的点击预测算法相比,LRCS在点击预测上有一定的性能提升。展开更多
基金support from the National Science and Technology Council of Taiwan(Contract Nos.111-2221 E-011081 and 111-2622-E-011019)the support from Intelligent Manufacturing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan,which is a Featured Areas Research Center in Higher Education Sprout Project of Ministry of Education(MOE),Taiwan(since 2023)was appreciatedWe also thank Wang Jhan Yang Charitable Trust Fund(Contract No.WJY 2020-HR-01)for its financial support.
文摘This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.
基金support provided by the National Natural Science Foundation of China(22122802,22278044,and 21878028)the Chongqing Science Fund for Distinguished Young Scholars(CSTB2022NSCQ-JQX0021)the Fundamental Research Funds for the Central Universities(2022CDJXY-003).
文摘To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.
文摘This paper presents an effective decision making framework for software selection in manufacturing industries using a multiple criteria decision making method, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The method is improved in the present work by integrating with analytic hierarchy process (AHP) and the fuzzy logic. Fuzzy logic is introduced to handle the imprecision of the human decision making process. The proposed decision making framework is practical for ranking competing software products in terms of their overall performance with respect to multiple criteria. An example is included to illustrate the approach.
文摘随着互联网和广告平台的飞速发展,面对海量的广告信息,为了提升用户点击率,提出一种改进的基于组合结构的逻辑回归点击预测算法LRCS(Logical Regression of Combination Structure)。该算法基于不同类别特征广告受众可能不同的特点,首先,采用FM进行特征组合,产生两类组合特征;其次,将一类特征组合作为聚类算法的输入进行聚类;最后,将另一类特征组合输入由聚类产生的分段GBDT+逻辑回归组合的模型中进行预测。在两个公开数据集中进行了多角度验证,结果表明与其他几类常用的点击预测算法相比,LRCS在点击预测上有一定的性能提升。
