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Actualities and Development of Heavy-Duty CNC machine Tool Thermal Error Monitoring Technology

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Chinese Journal of Mechanical engineering 2017年第5期30卷 1262-1281页

作者： Zu-De Zhou Lin Gui Yue-Gang Tan Ming-Yao Liu Yi Liu Rui-Ya Li School of Mechanical and Electronic engineering Wuhan University of Technology WHUT-Birmingham University Intelligent machine Joint Laboratory Hubei Digital Manufacturing Key Laboratory Wuhan Heavy Duty machine Tool Group Corporation

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC （computer numerical control） machine tools. Currently, there are many review literatures intro- ducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature moni- toring technology and thermal deformation monitoring technology. A new optical measurement technology called the ＂fiber Bragg grating （FBG） distributed sensing tech- nology＂ for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articlesto guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

关键词： Heavy-duty CNC machine tool Thermalerror Temperature field Deformation field Fiber Bragggrating

Scenario-based Unit Commitment Optimization for Power System with Large-scale Wind Power Participating in Primary Frequency Regulation

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1259-1267页

作者： Lili Hao Jing Ji Dongliang Xie Haohao Wang Wei Li Philip Asaah the School of Electrical engineering and control Science Nanjing Tech UniversityNanjing 211816China State Grid Electric Power Research institute Nanjing 211106China

Continuous increase of wind power penetration brings high randomness to power system,and also leads to serious shortage of primary frequency regulation(PFR)reserve for power system whose reserve capacity is typically provided by conventional *** large-scale wind power participating in PFR,this paper proposes a unit commitment optimization model with respect to coordination of steady state and transient *** addition to traditional operation costs,losses of wind farm de-loaded operation,environmental benefits and transient frequency safety costs in high-risk stochastic scenarios are also considered in the ***,the model makes full use of interruptible loads on demand side as one of the PFR reserve sources.A selection method for high-risk scenarios is also proposed to improve the calculation ***,this paper proposes an inner-outer iterative optimization method for the model *** method is validated by the New England 10-machine system,and the results show that the optimization model can guarantee both the safety of transient frequency and the economy of system operation.

关键词： Unit commitment optimization primary frequency regulation(PFR) wind power transient frequency safety high-risk stochastic scenario inner-outer iterative optimization

A Chemical Kinetics Perspective on the High-Temperature Oxidation of Methane and Propane through Experiments and Kinetic Analysis

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China Petroleum Processing & Petrochemical Technology 2022年第4期24卷 1-11页

作者： Ma Shoutao Yang Zhe Zhu Yunfeng Sun Bing Jiang Jie Xu Wei Meng Ruiji State Key Laboratory of Safety and control for Chemicals SINOPEC Research Institute of Safety EngineeringQingdao Shandong 266101China

In the conversion of methane and propane under high temperature and pressure,the ignition delay time(IDT)is a key parameter to consider for designing an inherently safe *** this study,the IDT characteristics of methane and propane(700–1000 K,10–20 bar)were studied experimentally and using kinetic modeling tools at stoichiometric fuel-tooxygen *** the experiments were conducted through insentropic *** reliable experimental data were obtained by using the adiabatic core hypothesis,which can be used to generate and validate the detailed chemical kinetics *** IDTs of methane and propane were recorded by a rapid compression machine(RCM)and compared to the predicted values obtained by the NUIGMech 3.0 *** test the applicability of NUIGMech 3.0 under different reaction conditions,the influence of temperature in the range of 700–1000 K(and the influence of pressure in the range of 10–20 bar)on the IDT was *** results showed that NUIGMech 3.0 could reasonably reproduce the experimentally determined IDT under the wide range of conditions *** constant volume chemical kinetics model was used to reveal the effect of temperature on the elementary reaction,and the negative temperature coefficient(NTC)behavior of propane was also observed at 20 *** experimental data can serve as a reference for the correction and application of kinetic data,as well as provide a theoretical basis for the safe conversion of low-carbon hydrocarbon chemicals.

关键词： ignition delay time NUIGMech 3.0 negative temperature coefficient kinetic analysis

Rapid Evaluation for Feed-axis Lubrication Condition Based on Soft Sensor

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Journal of Donghua University(English Edition) 2014年第6期31卷 843-846页

作者：周玉清母勇民刘建书章云 Xi'an institute of Modern control Technology Department of Automation Tsinghua University School of Mechanical engineering Xidian University

Stribeck effect is regarded as the most important feed-axis friction characteristics. According to the relationship between friction and lubrication,a rapid technology for feed-axis lubrication condition evaluation of computer numerical control( CNC) machine tools based on soft sensor is proposed. To obtain its state information,the static friction force,Coulomb friction force,and viscous coefficient are used as the key parameters of the soft sensor for tread analysis. Then the various amplitude and velocity triangular wave test curve, and a precise nonlinear model identification method are presented. The results of the experiments analysis show that this method is feasible and reliable for evaluating feed-axis lubrication condition,which lays the foundation for on-line condition monitoring and reliability evaluation for feed-axis lubrication of machine tools.

关键词： machine tool feed axis soft sensor lubrication condition Stribeck effect reliability

A multi-class large margin classifier

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Journal of Zhejiang University-Science A(Applied Physics & engineering) 2009年第2期10卷 253-262页

作者： Liang TANG Qi XUAN Rong XIONG Tie-jun WU Jian CHU institute of Cyber-Systems and control Zhejiang University Hangzhou 310027 China

Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.

关键词： Multi-classification Support vector machine (SVM) Quadratic programming (QP) problem Large margin

Recent advances on micro-control for near-critical complex systems

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control Theory and Technology 2022年第2期20卷 287-290页

作者： Di Zhao Yi Dong Department of control Science and engineering&Shanghai institute of Intelligent Science and Technology Tongji UniversityShanghaiChina

In this paper,we review some existing control methodologies for complex systems with particular emphasis on those that are near *** to the shortage of the classical control theory in handling complex systems,the reviewed control methods are mainly associated with machine learning techniques,game-theoretical approaches,and sparse control ***,several interesting and promising directions for future research are also proposed.

关键词： Complex systems Near criticality Micro-control machine learning Game theory Sparse control

Research on Performance Optimization of Spark Distributed Computing Platform

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Computers, Materials & Continua 2024年第5期79卷 2833-2850页

作者： Qinlu He Fan Zhang Genqing Bian Weiqi Zhang Zhen Li School of Information and control engineering Xi’an University of Architecture and TechnologyXi’an710054China Shaanxi institute of Metrology Science Xi’an710043China

Spark,a distributed computing platform,has rapidly developed in the field of big *** in-memory computing feature reduces disk read overhead and shortens data processing time,making it have broad application prospects in large-scale computing applications such as machine learning and image ***,the performance of the Spark platform still needs to be *** a large number of tasks are processed simultaneously,Spark’s cache replacementmechanismcannot identify high-value data partitions,resulting inmemory resources not being fully utilized and affecting the performance of the Spark *** address the problem that Spark’s default cache replacement algorithm cannot accurately evaluate high-value data partitions,firstly the weight influence factors of data partitions are modeled and ***,based on this weighted model,a cache replacement algorithm based on dynamic weighted data value is proposed,which takes into account hit rate and data *** integration and usage strategies are implemented based on LRU(LeastRecentlyUsed).Theweight update algorithm updates the weight value when the data partition information changes,accurately measuring the importance of the partition in the current job;the cache removal algorithm clears partitions without useful values in the cache to releasememory resources;the weight replacement algorithm combines partition weights and partition information to replace RDD partitions when memory remaining space is ***,by setting up a Spark cluster environment,the algorithm proposed in this paper is experimentally *** have shown that this algorithmcan effectively improve cache hit rate,enhance the performance of the platform,and reduce job execution time by 7.61%compared to existing improved algorithms.

关键词： Spark memory optimization memory replacement strategy

Accelerated Recursive Feature Elimination Based on Support Vector machine for Key Variable Identification

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Chinese Journal of Chemical engineering 2006年第1期14卷 65-72页

作者：毛勇皮道映刘育明孙优贤 National Laboratory of Industrial control Technology Institute of Modern Control Engineering Zhejiang University Hangzhou 310027 China National Laboratory of Industrial control Technology Institute of System Control Engineering Zhejiang University Hangzhou 310027 China

Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.

关键词： variable selection support vector machine recursive feature elimination fault diagnosis

Reinforcement learning with partitioning function system

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Journal of Harbin institute of Technology(New Series) 2004年第4期11卷 377-381页

作者：李伟叶庆泰朱昌明 College of machine and Dynamics engineering Shanghai Jiaotong University Shanghai 200030 China

The size of state-space is the limiting factor in applying reinforcement learning algorithms to practical cases. A reinforcement learning system with partitioning function (RLWPF) is established, in which state-space is partitioned into several regions. Inside the performance principle of RLWPF is based on a Semi-Markov decision process and has general significance. It can be applied to any reinforcement learning with a large state-space. In RLWPF, the partitioning module dispatches agents into different regions in order to decrease the state-space of each agent. This article proves the convergence of the SARSA algorithm for a Semi-Markov decision process, ensuring the convergence of RLWPF by analyzing the equivalence of two value functions in two Semi-Markov decision processes before and after partitioning. This article can show that the optimal policy learned by RLWPF is consistent with prior domain knowledge. An elevator group system is devised to decrease the average waiting time of passengers. Four agents control four elevator cars respectively. Based on RLWPF, a partitioning module is developed through defining a uniform round trip time as the partitioning criteria, making the wait time of most passengers more or less identical then elevator cars should only answer hall calls in their own region. Compared with ordinary elevator systems and reinforcement learning systems without partitioning module, the performance results show the advantage of RLWPF.

关键词： multi-agent systems partitioning reinforcement learning elevator