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Toward the optimal selective maintenance for multi-component systems using observed failure: applied to the FMS study case

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第1-4期96卷 1093-1107页

作者： Kammoun, Mohamed Ali Rezg, Nidhal Univ Lorraine Ind Engn Prod & Maintenance Lab Metz France ICN Business Sch Metz France

Several industrial systems are required to carry out a sequence of missions in which the maintenance activity is allowed only between two consecutive missions. In such case, the selective maintenance strategy is widely adopted, which aims to select the best components to be maintained, taking into account the constrained maintenance resources. The suggested maintenance selective resolution methods in the literature are based essentially on theoretical analyses and estimated parameters, which does not necessarily reflect the reality. In this study, we propose to combine techniques based on data mining approach to the selective maintenance of a multi-component system, using the maintenance data collection. For this purpose, the similar components are clustered, as a first step, and the age degradation coefficient of similar components is computed by using the K-means clustering algorithm. Next, we propose a mixed integer programming model that uses as input data the components degradation coefficients of the first step, in order to decide which components need to be replaced first. After, using the Apriori algorithm, the frequent sequences of maintained component are extracted, which provides us additional knowledge to predict the next maintenance activity. Finally, the proposed approach is assessed through a real-world case study of maintenance data of a flexible manufacturing system.

关键词： Selectiv emaintenance Multi-component systems Data mining clustering algorithm Association rules

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Research on Fast and Parallel clustering Method for Trajectory data 24

Research on Fast and Parallel Clustering Method for Trajecto...

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24th IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wang, Ne Gao, Shu Peng, Xiangwen Wang, Minrui Wuhan Univ Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538673089

In the era of big data, the development of satellite technology and Internet of Things has produced a large amount of trajectory data. We can effectively understand and predict the movement of the objects by analyzing their trajectory data. Now, most of density-based clustering algorithms have some disadvantages including the difficulty to determine input parameters, large 110, and so on. DPC (clustering by fast search and find of Density Peaks) is a new density-based clustering algorithm, which is simple and has only one input parameter, and also it is not affected by the data dimension, therefore, it can be effectively applied for trajectory clustering. However, in DPC, the local density is complex to calculate, and the cutoff distance is subjective to determine. In addition, DPC does not consider the existence of multiple cluster centers in the same cluster when clustering. To solve these problems, in this paper a fast clustering algorithm for trajectory data is put forward. In addition, Spark memory computing technology and data partitioning method are used to parallelize the algorithm, which greatly improves the clustering efficiency. Finally, experiments with three months' ship trajectory data from the Yangtze River have demonstrated that the clustering efficiency and effectiveness of our algorithm are significantly improved.

关键词： trajectory data density peak clustering algorithm parallel algorithms Spark

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Analyzing scientific context of researchers and communities by using complex network and semantic technologies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 89卷 584-605页

作者： Horta, Vitor Stroele, Victor Braga, Regina David, Jose Maria N. Campos, Fernanda Fed Univ Juiz de Fora UFJF Comp Sci Postgrad Program Knowledge Engn Res Grp Juiz De Fora MG Brazil

Social network communities are composed of people with common interests who influence or are influenced by themselves. In the scientific context, Scientific Social Networks are characterized as social networks that represent the social relations established by researchers. Identifying and exploring these relationships are fundamental activities to support scientific experiments. In this study, we aim to discuss the use of complex networks combined with semantic analysis in a network of scientific publications called DBLP. DBLP can be classified as big data, and its use for the analysis of connections and influences among researchers can be considered a context-aware approach. Therefore, in the present study, concepts of complex network analysis are applied to verify the level of influence among researchers, by analyzing the structure of the scientific social network under study and its communities. A bidirectional graph-based model was proposed in order to evaluate the influence of researchers, in addition to algorithms to analyze the network structure and identify scientific communities, using ontological terms and rules, considering the scientific context, and identifying new connections to promote scientific collaboration. For the identification of scientific communities, we proposed an overlapping community detection algorithm, named NetSCAN. A large scientific database (DBLP) together with digital libraries were used to evaluate the model and the algorithms in a historical research experiment. The results point to the viability and effectiveness of the proposed solution. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Scientific social network analysis Semantic analysis Overlapping community detection clustering algorithm Ontology Scientific context

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Alleviation of post-contingency overloads by SOCP based corrective control considering TCSC and MTDC

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第9期12卷 2155-2164页

作者： Bi, Ruyu Lin, Tao Chen, Rusi Ye, Jing Zhou, Xueming Xu, Xialing Wuhan Univ Sch Elect Engn Wuhan Hubei Peoples R China State Grid Hubei Elect Power Res Inst Wuhan Hubei Peoples R China State Grid Corp China Cent China Branch Wuhan Hubei Peoples R China

With the increasing installation of the high-voltage direct current device and flexible AC transmission system, the power transfer capability increases significantly. However, the risk of line overloads or even cascading failures caused by DC blocking or line outage also augments, which requires accurate and effective post-contingency corrective control (CC) actions. Traditional sensitivity-based methods are fast, but are less accurate and may suffer from the seesaw problem. Artificial intelligence optimisation-based methods usually exhibit better CC performance but are time-consuming. For fastly performing accurate CC actions, a second-order cone programming (SOCP)-based CC optimisation model for line overloads alleviation in meshed AC/DC power systems is proposed. Initially, to reduce the number of CC actions and optimisation variables (OVs), a heuristic search method is proposed to automatically select the most effective generators/loads for the overloaded lines. Subsequently, thyristor controlled series capacitor (TCSC) compensation levels and multi-terminal direct current (MTDC) power are treated as additional OVs to minimise load shedding/generation rescheduling. Additionally, to boost computational efficiency, SOCP format power flow equations for systems with MTDCs and TCSCs are proposed. Eventually, the effectiveness, accuracy, and superiority in computational efficiency of the proposed method are verified based on the modified IEEE 30-bus test system.

关键词： DC power transmission thyristor applications mathematical programming minimisation flexible AC transmission systems failure analysis power generation reliability power transmission reliability power generation scheduling renewable energy sources search problems pattern clustering load shedding load flow power transmission control power generation control post-contingency overload alleviation SOCP based corrective control TCSC MTDC high-voltage direct current device flexible AC transmission system smart grid cascading failures DC blocking post-contingency corrective control large-scale renewable energy generation integration artificial intelligence optimisation-based method second-order cone programming CC optimisation model SOCP-based CC optimisation model meshed AC-DC power systems optimisation variables OV heuristic search method sensitivity index clustering algorithm overloaded lines thyristor controlled series capacitor compensation levels multiterminal direct current power load shedding minimisation generation rescheduling minimisation power flow equations computational efficiency IEEE 30-bus test system line outage

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Sample plan selection techniques for lithography process model building

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JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS 2018年第4期17卷 1-12页

作者： Kumar, Pardeep Srinivasan, Babji Mohapatra, Nihar R. Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

The reduction of measurement data and the reduction of time required to select the sample plan are essential for the development of an efficient lithography process model. We have discussed the strengths and weaknesses of existing sample plan selection techniques and proposed a locally linear embedding (LLE)-based sample selection technique. The proposed approach significantly reduces the demand for metrology data and improves the modeling turn-around time without sacrificing the model accuracy and stability. The effectiveness of the proposed methodology is verified by modeling pattern transfer process of critical layers in 14- and 22-nm complementary metal-oxide-semiconductor technologies. The experimental results show that among different sample plan selection techniques, the LLE provides a competitive sample plan choice in a single shot without compromising the accuracy. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： lithography process model sample plan clustering algorithm high-dimensional space CM1 model locally linear embedding automatic sample planning and review principal component analysis optical proximity correction

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Construction and optimization of adaptive well pattern based on reservoir anisotropy and uncertainty

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 181卷 106252-000页

作者： Zhang, Hao Zhang, Kai Zhang, Liming Sheng, James Yao, Jun Wang, Jian Yang, Yongfei China Univ Petr 66 Changjiang West Rd Qingdao 266555 Shandong Peoples R China Texas Tech Univ 2500 Broadway Lubbock TX 79409 USA

The design of well pattern is of great significance to oilfield development. In view of the shortcomings of traditional well pattern, this paper proposes a new adaptive well pattern construction method. By using an unstructured meshing algorithm, large-scaled high-quality triangular and quadrangular adaptive well patterns are generated, which can effectively deal with various complex reservoir conditions. Existing wells and irregular boundary conditions are constrained and used to obtain the optimal well spacing. Thereby, a new vector well pattern is constructed by making a vector transformation to the adaptive well pattern. Based on this, a well pattern optimization method is proposed, in which NPV is set to be the objective function and key points as the optimization variables. The well pattern after optimization can perfectly adapt to reservoir geological anisotropy and heterogeneity, recovering more oil with fewer wells. In addition, reservoir uncertainty is also taken into consideration. Reservoir stochastic models are generated, and the expectation and standard deviation of the NPV values are used to construct the objective function. In coping with the huge calculation amount caused by reservoir uncertainty, a fuzzy C-mean clustering algorithm (FCM) & loop optimization method is combined to ensure the accuracy of optimization while reducing inefficient calculation. In the end, two examples are adopted to examine the reliability of the method proposed. The results show that the well pattern can perfectly adapt to anisotropic and uncertain reservoirs, and the optimization method can remarkably reduce the computational amount while ensuring the accuracy of the results.

关键词： Unstructured mesh Adaptive well pattern Vector well pattern clustering algorithm Uncertainty optimization

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A novel air quality evaluation method based on AP clustering and VAE model 30

A novel air quality evaluation method based on AP clustering...

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30th Chinese Control and Decision Conference, CCDC 2018

作者： Liang, Chen Sheng, Jin Yuxuan, Zhuang Haichao, Xu School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

ISBN: (纸本)9781538612439

At present, we assess the air quality levels based on AQI which requires complex calculations and cannot evaluate the air quality comprehensively and effectively. Our air quality evaluation method not only can cluster air quality data precisely, but also can identify the air quality using unsupervised learning without any prior knowledge. We collect 2600 days' of air pollutants data from baoding, suzhou and sanya which are clustered into 11 categories. A comparison between our clustering results and the air quality levels according to the traditional algorithm has been made. Moreover, we can identify a new air quality data set with the accuracy of about 96.5% based on variational autoencoder(VAE) model. This research will help people assess the air quality with air pollutants indexes more conveniently and scientificly. © 2018 IEEE.

关键词： air quality level clustering algorithm Variational Auto-Encoder

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Development of Efficient Strategy for Square Peg-in-Hole Assembly Task

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2018年第9期19卷 1323-1330页

作者： Kim, Keonwoo Kim, Jehyeok Seo, TaeWon Kim, Hwa Soo Kim, Jongwon Seoul Natl Univ Sch Mech Engn 1 Gwanak Ro Seoul 08826 South Korea Hanyang Univ Sch Mech Engn 222 Wangsimni Ro Seoul 04763 South Korea Kyonggi Univ Dept Mech Syst Engn 154-42 Gwanggyosan Ro Suwon 16227 Gyeonggi Do South Korea

In this paper, we present a dexterous gripper with an angle measurement system and efficient strategy for estimating a hole position from reaction force measurements in a square peg-in-hole task. The contact conditions between a square peg and hole have been analyzed to show that the redundancy in reaction force-moment relation around hole originated from contact conditions. In order to remove such redundancy in contact conditions, an angular error between a square peg and hole is first corrected with the help of the angle measurement system. Then, the positional error between them is identified by applying two clustering algorithms (K means algorithm and Gaussian Mixture Model (GMM) algorithm) to the reaction moment data obtained by the XY search. For grasping and manipulating a peg precisely, a 4-DOF dexterous gripper is designed, which consists of two fingers equipped with 6-axis force/torque sensor at fingertip. The extensive experiments using the proposed gripper with the angle measurement system demonstrate that during the square peg-in-hole task, the proposed strategy ensures the tact time of around 5 sec with the estimation accuracy of less than 0.35 mm and the deviation of less than 0.44 mm, respectively.

关键词： Robotic assembly Square peg-in-hole task Error recovery Blind search clustering algorithm

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Cluster-based generalized multiscale finite element method for elliptic PDEs with random coefficients

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JOURNAL OF COMPUTATIONAL PHYSICS 2018年 371卷 606-617页

作者： Chung, Eric T. Efendiev, Yalchin Leung, Wing Tat Zhang, Zhiwen Chinese Univ Hong Kong Dept Math Hong Kong Hong Kong Peoples R China Texas A&M Univ Dept Math College Stn TX 77843 USA Texas A&M Univ ISC College Stn TX 77843 USA Univ Hong Kong Dept Math Hong Kong Hong Kong Peoples R China

We propose a generalized multiscale finite element method (GMsFEM) based on clustering algorithm to study the elliptic PDEs with random coefficients in the multi-query setting. Our method consists of offline and online stages. In the offline stage, we construct a small number of reduced basis functions within each coarse grid block, which can then be used to approximate the multiscale finite element basis functions. In addition, we coarsen the corresponding random space through a clustering algorithm. In the online stage, we can obtain the multiscale finite element basis very efficiently on a coarse grid by using the precomputed multiscale basis. The new GMsFEM can be applied to multiscale SPDE starting with a relatively coarse grid, without requiring the coarsest grid to resolve the smallest-scale of the solution. The new method offers considerable savings in solving multiscale SPDEs. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed method for several multiscale stochastic problems without scale separation. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Stochastic partial differential equations (SPDEs) Generalized multiscale finite element method (GMsFEM) Multiscale basis functions Karhunen-Loeve expansion Uncertainty quantification (UQ) clustering algorithm

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I-nice: A new approach for identifying the number of clusters and initial cluster centres

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INFORMATION SCIENCES 2018年 466卷 129-151页

作者： Masud, Md Abdul Huang, Joshua Zhexue Wei, Chenghao Wang, Jikui Khan, Imran Zhong, Ming Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China

This paper proposes I-nice, which is a new method for automatically identifying the number of clusters and selecting the initial cluster centres in data. The method mimics a human being in observing peaks of mountains in field observation. The clusters in a dataset are considered as the hills in a field terrain. The distribution of distances between the observation point and the objects is computed. The distance distribution is modelled by a set of Gamma mixture models (GMMs), which are solved with the expectation-maximization (EM) algorithm. The best-fitted model is selected with an Akaike information criterion variant (AICc). In the I-niceSO algorithm, the number of components in the model is taken as the number of clusters, and the objects in each component are analysed with the k-nearest-neighbour method to find the initial cluster centres. For complex data with many clusters, we propose the I-niceMO algorithm, which combines the results of multiple observation points. Experimental results show that the two algorithms significantly outperformed two state-of-the-art methods (Elbow and Silhouette) in identifying the correct number of clusters in data. The results also show that I-niceMO improved the clustering accuracy and efficiency of the k-means clustering process. (C) 2018 Elsevier Inc. All rights reserved.

关键词： clustering algorithm Initial cluster centres Number of clusters

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