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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿(英文)》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要: Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词: deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

《机械工程前沿(英文)》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0703-5

摘要: In fiber laser beam welding (LBW), the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality. This study proposes a multi-objective optimization framework by combining an ensemble of metamodels (EMs) with the multi-objective artificial bee colony algorithm (MOABC) to identify the optimal welding parameters. An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM, which incorporates the competitive strengths of three metamodels. EM constructs the correlation between processing parameters (laser power, welding speed, and distance defocus) and bead geometries (bead width, depth of penetration, neck width, and neck depth) with average errors of 10.95%, 7.04%, 7.63%, and 8.62%, respectively. On the basis of EM, MOABC is employed to approximate the Pareto front, and verification experiments show that the relative errors are less than 14.67%. Furthermore, the main effect and the interaction effect of processing parameters on bead geometries are studied. Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.

关键词: laser beam welding     parameter optimization     metamodel     multi-objective    

1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

《能源前沿(英文)》 2008年 第2卷 第2期   页码 187-193 doi: 10.1007/s11708-008-0030-5

摘要: The 2 × 1000 MW ultra-supercritical steam turbine of Shanghai Waigaoqiao Phase III project, which uses grid frequency regulation and overload control through an overload valve, is manufactured by Shanghai Turbine Company using Siemens technology. Through optimization, the steam pressure is regarded as the criterion between constant pressure and sliding pressure operation. At high circulating water temperature, the turbine overload valve is kept closed when the unit load is lower than 1000 MW while at other circulating water temperatures the turbine can run in sliding pressure operation when the unit load is higher than 1000 MW and the pressure is lower than 27 MPa This increases the unit operation efficiency. The 3D bending technology in the critical piping helps to reduce the project investment and minimize the reheat system pressure drop which improves the unit operation efficiency and safety. By choosing lower circulating water design temperature and by setting the individual Boiler Feedwater Turbine condenser to reduce the exhaust steam flow and the heat load to the main condenser, the unit average back pressure and the terminal temperature difference are minimized. Therefore, the unit heat efficiency is increased.

Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

《机械工程前沿(英文)》 2021年 第16卷 第2期   页码 221-248 doi: 10.1007/s11465-020-0627-x

摘要: Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

关键词: energy efficiency     cutting parameter     optimization     machining process    

Ant colony optimization for assembly sequence planning based on parameters optimization

Zunpu HAN, Yong WANG, De TIAN

《机械工程前沿(英文)》 2021年 第16卷 第2期   页码 393-409 doi: 10.1007/s11465-020-0613-3

摘要: As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.

关键词: assembly sequence planning     ant colony optimization     symbiotic organisms search     parameter optimization    

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

《结构与土木工程前沿(英文)》 2023年 第17卷 第1期   页码 25-36 doi: 10.1007/s11709-022-0908-z

摘要: In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词: tunnel boring machine     control parameter optimization     quantum particle swarm optimization     artificial neural network     tunneling energy efficiency    

Optimization of power and efficiency for an irreversible Diesel heat engine

Shiyan ZHENG, Guoxing LIN

《能源前沿(英文)》 2010年 第4卷 第4期   页码 560-565 doi: 10.1007/s11708-010-0018-9

摘要: A cyclic model of an irreversible Diesel heat engine is presented, in which the heat loss between the working fluid and the ambient during combustion, the irreversibility inside the cyclic working fluid resulting from friction, eddies flow, and other irreversible effects are taken into account. By using the thermodynamic analysis and optimal control theory methods, the analytical expressions of power output and efficiency of the Diesel heat engine are derived. Variations of the main performance parameters with the pressure ratio of the cycle are analyzed and calculated. The optimum operating region of the heat engine is determined. Moreover, the optimum criterion of some important parameters, such as the power output, efficiency, pressure ratio, and temperatures of the working fluid at the related state points are illustrated and discussed. The conclusions obtained in the present paper may provide some theoretical guidance for the optimal parameter design of a class of internal-combustion engines.

关键词: Diesel heat engine     irreversibility     power output     efficiency     parameter optimization    

Intelligent methods for the process parameter determination of plastic injection molding

Huang GAO, Yun ZHANG, Xundao ZHOU, Dequn LI

《机械工程前沿(英文)》 2018年 第13卷 第1期   页码 85-95 doi: 10.1007/s11465-018-0491-0

摘要:

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system-based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

关键词: injection molding     intelligent methods     process parameters     optimization    

Design optimization of a wind turbine gear transmission based on fatigue reliability sensitivity

Genshen LIU, Huaiju LIU, Caichao ZHU, Tianyu MAO, Gang HU

《机械工程前沿(英文)》 2021年 第16卷 第1期   页码 61-79 doi: 10.1007/s11465-020-0611-5

摘要: Fatigue failure of gear transmission is one of the key factors that restrict the performance and service life of wind turbines. One of the major concerns in gear transmission under random loading conditions is the disregard of dynamic fatigue reliability in conventional design methods. Various issues, such as overweight structure or insufficient fatigue reliability, require continuous improvements in the reliability-based design optimization (RBDO) methodology. In this work, a novel gear transmission optimization model based on dynamic fatigue reliability sensitivity is developed to predict the optimal structural parameters of a wind turbine gear transmission. In the model, the dynamic fatigue reliability of the gear transmission is evaluated based on stress–strength interference theory. Design variables are determined based on the reliability sensitivity and correlation coefficient of the initial design parameters. The optimal structural parameters with the minimum volume are identified using the genetic algorithm in consideration of the dynamic fatigue reliability constraints. Comparison of the initial and optimized structures shows that the volume decreases by 3.58% while ensuring fatigue reliability. This work provides new insights into the RBDO of transmission systems from the perspective of reliability sensitivity.

关键词: gear transmission     fatigue reliability     reliabi-lity sensitivity     parameter optimization    

Restoration of hyper-eutrophic water with a modularized and air adjustable constructed submerged plant

Jinzhong LI, Xueju LI, Shujuan SUN, Xuegong LIU, Suiliang HUANG

《环境科学与工程前沿(英文)》 2011年 第5卷 第4期   页码 573-584 doi: 10.1007/s11783-011-0363-x

摘要: A modularized and air adjustable constructed submerged plant bed (CSPB) which can be used to restore the eutrophic water is introduced in this paper. This plant bed helps hydrophyte grow under poor conditions such as frequently changed water depth, impaired water transparency, algae bloom and substantial duckweed in summer, which are not naturally suitable for growing hydrophyte. This pilot study in Waihuan River of Tianjin, China, revealed that reduction of Chemical Oxygen Demand (COD), Total Nitrogen (TN) and Total Phosphorus (TP) by the use of CSPB could be reached 30%–35%, 35%–40%, 30%–40% respectively in the growing season (from March to October) and 5%–10%, 5%–15%, 7%–20% respectively in the winter (from November to February) when the detention time was 6 d. The relationships between the concentration of COD, TN, TP and the detention time fit the first-order kinetic equation well and the coefficients of determination ( ) were all above 0.9. The attenuation coefficients k of the kinetic equation were a function of the water temperature. When the water temperature was quite low or quite high, was not significantly changed with increasing or decreasing water temperature. While when the temperature was in a moderate range, an increase or decrease of water temperature would lead to a rapid increase or decrease in .

关键词: modularized and air adjustable constructed submerged plant bed     water purification     eco-restoration techniques     aquatic plants     eutrophication    

一种基于参数扰动的芯片成品率双目标优化框架

Xin LI,Jin SUN,Fu XIAO,Jiang-shan TIAN

《信息与电子工程前沿(英文)》 2016年 第17卷 第2期   页码 160-172 doi: 10.1631/FITEE.1500168

摘要:

随着收缩技术的发展,工艺,电压和温度(PVT)参数的可变性显着影响了芯片设计的成品率分析和优化。先前的产量估计算法已经限于预测时序或功率产量。但是,忽略功率和延迟之间的相关性将导致明显的产量损失。这些方法中的大多数都还具有较高的计算复杂度和较长的运行时间。我们提出了一种基于Chebyshev仿射算术(CAA)和自适应加权和(AWS)方法的新型双目标优化框架,在该框架中将功率和时序收益两者均设置为目标函数。同时优化两个目标以保持它们之间的相关性。所提出的方法首先在任意相关性的假设下预测泄漏和延迟分布的保证概率边界。然后,通过计算累积分布函数(CDF)边界来建立功率延迟双目标优化模型。最后,将AWS方法应用于功率延迟优化,以生成分布良好的一组Pareto最优解。在ISCAS基准电路上的实验结果表明,该双目标框架能够在功率和时序产量之间提供足够的权衡信息。

关键词: 参数变化,参数收益率,多目标优化,切比雪夫仿射,自适应加权和,    

Dymola-based multi-parameters integrated optimization for high speed transfer system of LED chip sorter

Jie OUYANG, Bin LI, Shihua GONG

《机械工程前沿(英文)》 2013年 第8卷 第2期   页码 118-126 doi: 10.1007/s11465-013-0253-y

摘要:

To enhance the performance of high speed transfer system of LED chip sorting equipment, its control parameters need to be well matching with the mechanical system. In practical issues, it is difficult and time-consuming work to get these parameters matched because their selection is strongly depended on individuals. In current work, an integrated optimization method was carried out to solve this problem, in which the multiple control parameters optimization, modeling and simulation were included, i.e., a multi-domain model of transfer system performed on Dymola platform. Based on this model, the searching area of the key control parameters was narrowed by performing integrated optimization. After that a group of parameters were selected from this narrowed area to perform the equipment’s controls. The result showed this method possesses a simple and reliable nature. The optimal solutions also indicated that the optimized control parameters can well satisfy the requirements of transfer system. On the other hand, it greatly reduced the engineering adjustment time by using this method.

关键词: LED chip sorter     multi-domain modeling and simulation     parameter optimization     modelica language    

Preparation of a novel anion exchange group modified hyper-crosslinked resin for the effective adsorption

Qing ZHOU, Mengqiao WANG, Aimin LI, Chendong SHUANG, Mancheng ZHANG, Xiaohan LIU, Liuyan WU

《环境科学与工程前沿(英文)》 2013年 第7卷 第3期   页码 412-419 doi: 10.1007/s11783-013-0483-6

摘要: A novel hyper-crosslinked resin (MENQ) modified with an anion exchange group was prepared using divinylbenzene (DVB) and methyl acrylate (MA) as comonomers via four steps: suspension polymerization, post-crosslinking, ammonolysis and alkylation reactions. The obtained resin had both a high specific surface area (793.34 m ·g ) and a large exchange capacity (strong base anion exchange capacity, SEC: 0.74 mmol·g , weak base anion exchange capacity, WEC: 0.45 mmol·g ). XAD-4 was selected as an adsorbent for comparison to investigate the adsorption behavior of tetracycline (TC) and humic acid (HA) onto the adsorbents. The results revealed that MENQ could effectively remove both TC and HA. The adsorption capacity of XAD-4 for TC was similar to that of MENQ, but XAD-4 exhibited poor performance for the adsorption of HA. The adsorption isotherms of TC and HA were well-fitted with the Freundlich model, which indicated the existence of heterogeneous adsorption through cation-π bonding and π–π interactions. The optimal solution condition for the adsorption of TC was at a pH of 5–6, whereas the adsorption of HA was enhanced with increasing pH of the solution.

关键词: high surface area     adsorption     anion exchange     micropollutant     dissolved organic matters    

一种大偏心皮卫星分离参数复合优化方法 None

Lai TENG, Zhong-he JIN

《信息与电子工程前沿(英文)》 2018年 第19卷 第5期   页码 685-698 doi: 10.1631/FITEE.1700416

摘要: 航天飞行器的分离参数直接影响它的飞行轨迹,如果分离参数超过它能承受的极限,则飞行器难以调整飞行姿态,可能造成飞行器偏离轨道或坠毁。提出一种将角速度与外矩结合的大偏心皮卫星分离参数复合优化方法。通过改变弹性发射装置位置,在分离机构变化较小情况下,有效控制飞行器弹出过程。给出了角速度偏差的原因和不可信的优化结果,并对不可信的优化结果进行分析。通过地面无重力试验对该优化方法进行验证。仿真和试验结果表明,该优化方法能有效优化大偏心皮卫星的分离参数。该方法特别适用于固定和非稳定状态弹性参数、各种弹性装置的分布以及难以校正姿态的大偏心航天飞行器,在实际应用中具有通用性和易操作性。

关键词: 皮卫星;星箭分离机构;分离参数;参数优化    

基于GA-ANFIS在石灰矿技术经济系统中的参数优化研究与应用实践

杨仕教,戴剑勇,曾晟

《中国工程科学》 2005年 第7卷 第6期   页码 61-65

摘要:

为掌握水泥原料矿山系统中的技术经济参数对矿石成本影响的关联规律性,首先运用自适应模糊神经网络对矿山技术经济系统建模,再用并行遗传算法对模型求解,得到了确保矿石成本最小的各项最优技术经济指标,为提高矿山生产管理与经济效益提供了重要的参考价值。

关键词: 自适应模糊神经网络     并行遗传算法     技术经济参数    

标题 作者 时间 类型 操作

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

期刊论文

1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

期刊论文

Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

期刊论文

Ant colony optimization for assembly sequence planning based on parameters optimization

Zunpu HAN, Yong WANG, De TIAN

期刊论文

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

期刊论文

Optimization of power and efficiency for an irreversible Diesel heat engine

Shiyan ZHENG, Guoxing LIN

期刊论文

Intelligent methods for the process parameter determination of plastic injection molding

Huang GAO, Yun ZHANG, Xundao ZHOU, Dequn LI

期刊论文

Design optimization of a wind turbine gear transmission based on fatigue reliability sensitivity

Genshen LIU, Huaiju LIU, Caichao ZHU, Tianyu MAO, Gang HU

期刊论文

Restoration of hyper-eutrophic water with a modularized and air adjustable constructed submerged plant

Jinzhong LI, Xueju LI, Shujuan SUN, Xuegong LIU, Suiliang HUANG

期刊论文

一种基于参数扰动的芯片成品率双目标优化框架

Xin LI,Jin SUN,Fu XIAO,Jiang-shan TIAN

期刊论文

Dymola-based multi-parameters integrated optimization for high speed transfer system of LED chip sorter

Jie OUYANG, Bin LI, Shihua GONG

期刊论文

Preparation of a novel anion exchange group modified hyper-crosslinked resin for the effective adsorption

Qing ZHOU, Mengqiao WANG, Aimin LI, Chendong SHUANG, Mancheng ZHANG, Xiaohan LIU, Liuyan WU

期刊论文

一种大偏心皮卫星分离参数复合优化方法

Lai TENG, Zhong-he JIN

期刊论文

基于GA-ANFIS在石灰矿技术经济系统中的参数优化研究与应用实践

杨仕教,戴剑勇,曾晟

期刊论文