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Mission Reliability-Oriented Selective Maintenance optimization for Intelligent Multistate Manufacturing Systems With Uncertain Maintenance Quality

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IEEE ACCESS 2019年 7卷 109804-109816页

作者： Chen, Zhaoxiang He, Yihai Zhao, Yixiao Han, Xiao Liu, Fengdi Zhou, Di Wang, Wenzhuo Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Selective maintenance is widely used as a reliability-centered maintenance strategy due to the limited maintenance resources. However, existing selective maintenance studies only consider basic reliability, which cannot systematically describe the operating mechanism of a multistate system, thereby resulting in the inability to obtain an optimal maintenance strategy. Moreover, intelligent manufacturing systems are highly representative of typical multistate industrial systems. In this study, a mission reliability-oriented selective maintenance optimization model for intelligent manufacturing systems that considers the uncertain maintenance effect was proposed. First, a new connotation and modeling method for mission reliability based on multistate system theory was presented to comprehensively characterize the operating mechanism of intelligent manufacturing systems. Second, a quantitative model between maintenance resources and quality based on real-time data was established to reflect the uncertain characteristics caused by repairmen and tools. Third, a selective maintenance decision model of a multistate manufacturing system was developed under the constraints of maintenance cost and time. This constraint combination optimization problem was solved using the particle swarm optimization algorithm. Finally, a case study of selective maintenance optimization for a cylinder head manufacturing system was presented to verify the proposed method.

关键词： Multistate system manufacturing system selective maintenance mission reliability particle swarm optimization algorithm

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Human action tracking design of neural network algorithm based on GA-PSO in physical training

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4149-S4155页

作者： Leng Xinke Jiang Huijun Zuo Xin Hunan Univ Coll Phys Educ Changsha 410028 Hunan Peoples R China Hunan First Normal Coll Dept Phys Educ Changsha 410205 Hunan Peoples R China

In order to solve human action recognition algorithm in traditional physical training, the problem of some kind of action recognition without universality is usually solved emphatically, and a recognition system aimed at human action tracking in physical training is proposed. Firstly, local feature description of human action in physical training based on self-similarity matrix is constructed by using generalized self-similarity concept changed along with time and local feature extraction methods of optical flow field of spatio-temporal interest point;secondly, a new neural network training algorithm of particle swarm optimization (PSO) with self-adaptive genetic operator is proposed. Through probability control, at the same time of using PSO algorithm to optimize neural network, selection, intersection, variation and other genetic operation are implemented for optional particles adaptively to realize promotion of algorithm performance;simulation experiment indicates that proposed scheme can obviously improve algorithm efficiency of human action recognition and recognition accuracy in physical training.

关键词： Physical training Genetic algorithm particle swarm optimization algorithm Human action Tracking

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Fault Diagnosis and Reliable Configuration of Uncertain Strip Area Based on MPSO-SVM

Fault Diagnosis and Reliable Configuration of Uncertain Stri...

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第40届中国控制会议

作者： Mochen Li Bo Yao Fuzhong Wang College of Mathematics and System Science Shenyang Normal University Department of Basic Education Shenyang Institute of Engineering

For a class of linear uncertain flight control systems,in terms of the strip region,the problem of actuator continuous gain fault and reliable control is *** the same time,in order to solve the difficulty of selecting parameters of support vector machine,a parameter optimization algorithm based on improved particle swarm optimization(MPSO) is *** with grid search method,particle swarm optimization and genetic algorithm,this method has the advantages of fast convergence,short time consuming and high *** the problem that the pole information is difficult to obtain,the algorithm theory of pole observer is given to realize the real-time observation of unknown ***,the effectiveness of reliable controller design is further proved by numerical simulation.

关键词： reliable control support vector machine pole observer particle swarm optimization algorithm grid search method genetic algorithm

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Optimal Time of Use Electricity Pricing Model and Its Application to Electrical Distribution System

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IEEE ACCESS 2019年 7卷 123558-123568页

作者： Yang, Hejun Wang, Lei Ma, Yinghao Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China

Because the time of use (TOU) strategies can directly affect the power flow distribution of electrical distribution system, this paper investigates the optimal TOU electricity pricing model and its functions for improving the power quality and reducing the power loss of electrical distribution system. Firstly, an optimal period partitioning algorithm based on a moving boundary technique is proposed for dividing an entire day to the different periods. Secondly, an optimal TOU electricity pricing model is proposed through minimizing the peak-valley difference, the voltage fluctuation, and the power loss. The particle swarm optimization (PSO) algorithm is adopted to solve the proposed optimization problem, and the multi-objective constrained optimization problem is transformed into a single objective unconstrained optimization problem. Thirdly, two novel indices of describing the voltage variation and the power loss are defined for considering the impact of TOU strategies and improving the power quality and reducing the power loss. Finally, an IEEE 14-bus system is applied to verifying the correctness and effectiveness of the proposed method. The results prove that the algorithm which proposed in this paper has a great significance in improving the power quality and the economic benefits of electrical distribution system.

关键词： Time of use optimal period partitioning moving boundary technique particle swarm optimization algorithm electrical distribution system

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Bhattacharyya distance criterion based multibit quantizer design for cooperative spectrum sensing in cognitive radio networks

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WIRELESS NETWORKS 2019年第5期25卷 2665-2674页

作者： Fu, Yuanhua He, Zhiming Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Guangdong Inst Elect & Informat Engn Dongguan Peoples R China

Cooperative spectrum sensing (CSS) is crucial for dynamic spectrum access in cognitive radio networks. This paper considers a CSS scheme by using a multilevel quantizer in each sensing node (SN) to quantize the local energy detector's observation. A log-likelihood ratio test detector by using quantized data received from each SN is proposed to determine the presence or absence of the primary user signal. The Bhattacharyya distance (BD) of the cooperative sensing system is derived. Then, a quantizer design scheme by maximizing the BD that as the criterion function in an optimization problem with respect to the quantization thresholds is proposed. As nonlinear and high-dimensional of the objective function, a particle swarm optimization algorithm is employed to solve operating parameters for the quantizer. Furthermore, we derive the upper bound performance on cooperative energy detection (CED) in CSS. To validate the effectiveness of the proposed approach, The quantizer is compared with other schemes in terms of detection performance. Simulation results show that 2- or 3-bit quantization of the proposed approach achieves comparable performance to upper bound of CED without quantization.

关键词： Multibit quantization Bhattacharyya distance particle swarm optimization algorithm Cooperative spectrum sensing

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A New Two-Stage Approach for a Bi-Objective Facility Layout Problem Considering Input/Output Points Under Fuzzy Environment

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IEEE ACCESS 2019年 7卷 134083-134103页

作者： Mohamadi, Arash Ebrahimnejad, Sadoullah Soltani, Roya Khalilzadeh, Mohammad Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran *** Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj *** Iran KHATAM Univ Dept Ind Engn Tehran *** Iran

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts' opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.

关键词： Facility layout problem different preference representation structures goal programming genetic algorithm particle swarm optimization algorithm fuzzy sets

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Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV and an autonomous underwater vehicle (AUV) in an underwater target strike mission

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OCEAN ENGINEERING 2019年第Jun.15期182卷 162-173页

作者： Wu, Yu Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China

Unmanned system has become more and more popular as it can adapt to diverse environments and has prospective applications. Especially, the coordination among heterogeneous vehicles is capable of completing complicated tasks, which is often beyond the ability of homogeneous vehicles. In this paper, the underwater target strike mission is concentrated, and the mission is completed by the coordination between a UAAV and an AUV. UAAV and AUV are deployed in this mission because UAAV has strong search ability in air and can communicate with AUV directly after it dives into water. Firstly, to decompose the problem, the mission is divided into two phases, i.e., single flying of UAAV and underwater coordination between UAAV and AUV. In the coordinated path planning model, the motion of vehicles, the constraints in different media and the optimization index in each phase are all formulated into mathematical forms. Based on the particle swarm optimization (PSO) algorithm, the collocation points are used to determine the locations of control variables. Those points can reduce the computation load and improve the solution quality, and they are distributed by height and moment according to the forms of constraints in each phase. Besides, the strategy of addressing infeasible solutions is generated to guarantee the normal operation of PSO-based algorithm. Simulation results demonstrate that the proposed two-phase coordinated path planning method can generate coordinated paths, and the obtained results is very close to the optimal solution in theory. Compared to the whole method, the two-phase method can better deal with the complicated constraints in each phase.

关键词： Unmanned system Unmanned aerial-aquatic vehicle Autonomous underwater vehicle Coordinated path planning particle swarm optimization algorithm

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Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy

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NEUROCOMPUTING 2019年 332卷 159-183页

作者： Liu, Yang Chen, Shuangqing Guan, Bing Xu, Ping Northeast Petr Univ Sch Petr Engn Daqing 163318 Peoples R China

Layout optimization of large-scale oil-gas gathering system is a kind of NP-hard problem in the field of system optimization. It involves a large number of network nodes, coupled optimization variables, complex network structures and hydraulic constraints, which cause the great difficulty in constructing optimization models and solution methods. In this paper, a high-dimensional mixed integer nonlinear layout optimization mathematical model involving the pipeline network structure parameters and pipeline design parameters is established, which can be applied to large-scale oil gathering system and gas gathering system universally. A modified particle swarm optimization (MPSO) algorithm with global search ability is proposed. The convergence theorem of the stochastic optimization algorithm is established based on the Poincare cycle theory. Global convergence of MPSO algorithm is proved, and the performance of MPSO algorithm is analyzed by numerical experiments. Based on dimension reduction planning and modularization thought, the grid dissection set partition method is proposed, and the theoretical foundation and complexity of the grid dissection method are discussed. In order to reduce the dimension of the layout optimization problem, the concept of the fuzzy set of adjacent position, and a novel approach for the well-station connection mode optimization are put forward. Based on the MPSO algorithm, grid dissection set partition method and solution method by fuzzy set of adjacent position, a combined optimization strategy for layout optimization model is proposed. The reliability and practicality of the proposed layout optimization model and combined optimization strategy are verified by the successful application of a real-world large-scale oil field with 661 wells. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Layout optimization particle swarm optimization algorithm Global convergence Convergence theorem Oil-gas gathering system

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基于光纤光谱技术的羊肉掺假检测

基于光纤光谱技术的羊肉掺假检测

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作者：成甜甜河北农业大学

学位级别：硕士

Lamb meat is delicious and rich in nutritional *** is deeply loved by consumers and its demand at home and abroad continues to ***,because many mutton processing companies are difficult to manage,it provides opportunities for some illegal vendors,which has led to the intensification of mutton *** order to protect the rights and health of consumers,meat adulteration testing is *** present,some chemical methods have been used to detect whether meat is adulterated,but these methods have certain defects,which damage the meat and cause a certain waste of time or *** order to make up for the lack of chemical methods,the paper uses fiber optic spectroscopy to adulterate ***-depth research on detection *** main work of this paper is as follows:(1)Pretreatment of spectral ***,the spectral reflectance of the adulterated mutton samples was obtained based on the optical fiber spectroscopy ***,the original spectral data was preprocessed,and the Savitzky-Golay convolution smoothing method was adopted to reduce the noise of spectral data,smooth the spectral curve,and improve the signal to noise ratio of *** data with different dimensions are normalized to the interval[0,1]to make the data *** data were provided for the establishment of mutton adulteration detection model.(2)A qualitative detection model of mutton adulteration based on weighted random forest algorithm was *** sample can be divided into two categories:adulteration pure samples of mutton and mutton samples,adulterated mutton samples including "lamb-chicken" and "lamb-pork" adulterated samples,established the mutton adulterated detection model based on random forest algorithm,identify adulteration sample is pure lamb or mutton,results show that the model accuracy is 96.67%.Then further to identify mutton adding meat,sample can be divided into three categories:pure samples of mutton,"lamb-chicken" adulterated sample

关键词： Detection of mutton adulteration Random forest Least squares support vector machines particle swarm optimization algorithm

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A study of test and statistical damage constitutive model of multi-size polypropylene fiber concrete under impact load

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2019年第7期28卷 973-989页

作者： Yang, Xin Liang, Ninghui Liu, Xinrong Zhong, Zuliang Fujian Univ Technol Sch Civil Engn Fuzhou Fujian Peoples R China Fujian Prov Univ Key Lab Underground Engn Fuzhou Fujian Peoples R China Fujian Univ Technol Inst Geotech Engn Fuzhou Fujian Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing Peoples R China

To investigate the effects of mixing polypropylene fiber of different sizes and the effect of fiber size on the impact characteristics of concrete, two sizes of polypropylene fine fiber and one size of polypropylene coarse fiber were selected to design and fabricate nine groups of polypropylene fiber-reinforced concrete test specimens by controlling the fiber mixing ratio and conducting a split Hopkinson pressure bar test to obtain the stress-strain curves of the test specimens in various groups, and their parameters such as the elasticity modulus, peak strength, and peak strain. The incorporation of fiber improved the pre-peak impact properties of concrete to different extents. Strain hardening did not occur in the post-peak curves, and different types of fibers exhibited different characteristics. Thus, the fine fiber could significantly improve the peak strain, while the coarse fiber could more significantly improve the elasticity modulus and peak strength. The improving effects exerted by incorporating three types of fiber were better than those exerted by incorporating two types of fiber. Moreover, the statistical damage model was used to obtain the parameters by fitting and analyzing their variation rules based on the statistical damage constitutive model and the particle swarm optimization algorithm.

关键词： Different sizes polypropylene fiber-reinforced concrete split Hopkinson pressure bar test statistical damage constitutive model particle swarm optimization algorithm

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