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A multi-source information fusion method for tool life prediction based on CNN-SVM

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Mechanical Engineering Science 2022年第2期4卷 1-10,I0003,I0004页

作者： Shuo WANG Zhenliang YU Peng LIU Man Tong WANG School of Mechanical and Power Engineering Yingkou Institute of TechnologyYingkouChina

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction *** this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression *** results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction *** compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

关键词： CNN-SVM tool wear life prediction multi-source information fusion

information fusion diagnosis and early-warning method for monitoring the long-term service safety of high dams

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2012年第9期13卷 687-699页

作者： Xing LIU Zhong-ru WU Yang YANG Jiang HU Bo XU State Key Laboratory of Hydrology-Water'Resources and Hydraulic Engineering Hohai UniversityNanjing 210098China National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Hohai UniversityNanjing 210098China College of Water-Conservancy and Hydropower Hohai UniversityNanjing 210098China

Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term *** analysis methods used to obtain safety monitoring data are suited only to single survey point *** or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise ***,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high *** addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high *** metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive ***-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey *** can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.

关键词： Dam monitoring Diagnosis Early-warning multi-source information fusion information entropy

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An information-volume-based distance measure for decision-making

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Chinese Journal of Aeronautics 2023年第5期36卷 392-405页

作者： Zhanhao ZHANG Fuyuan XIAO School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy ***,the mass assignments given by unknown information sources are *** to measure the difference between the mass assignments has aroused people’s *** this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass *** method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass *** the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed information Volume Distance(IVD)through simulation ***,in the process of information fusion,the reliability of each source could be quantified through ***,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information ***,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.

关键词： Basic belief assignments Decision-making Distance measure Evidence theory multi-source information fusion

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Remaining useful life prediction for multi-component systems with hidden dependencies

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Science China(information Sciences) 2019年第2期62卷 57-72页

作者： Xiaopeng XI Maoyin CHEN Donghua ZHOU Department of Automation Tsinghua University College of Electrical Engineering and Automation Shandong University of Science and Technology

How can we predict the remaining useful life(RUL) of a dynamic system subject to multiple dependent degradations? Is it possible to address the above problem when the degradation information is obtained indirectly? According to a new type of state space-based model, we mainly develop an online RUL prediction method for the above system. In this model, the dependencies among different degradations can be reflected in a diffusion coefficient matrix. Considering that some industrial systems like blast furnaces are usually equipped with multi-sensors, an efficient information fusion strategy also plays an important role in predicting the RUL. Based on multi-dimensional observations, the hidden degradation states are identified through the sequential Kalman filtering. Meanwhile, the unknown parameters in the model are updated iteratively by the expectation maximization(EM) algorithm. At last, the RUL distributions are simulated through the Monte Carlo method, in which three types of failure structures with regard to the degradations are considered. The effectiveness of the proposed method is fully verified by a numerical example as well as a case study about the blast furnace.

关键词： remaining useful life hidden dependencies state space model multi-source information fusion failure structure

Structural damage detection method based on information fusion technique

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Journal of Southeast University(English Edition) 2008年第2期24卷 201-205页

作者：刘涛李爱群丁幼亮费庆国东南大学土木工程学院南京210096

multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

关键词： multi-source information fusion structural damage detection Bayes method D-S evidence theory

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Intelligent early-warning platform for open-pit mining: Current status and prospects

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Rock Mechanics Bulletin

Rock Mechanics Bulletin 2024年第1期3卷 16-36页

作者： Zhanping Song Xu Li Runke Huo Lianbaochao Liu School of Civil Engineering Xi'an University of Architecture and TechnologyXi'an710055China Shaanxi Key Lab of Geotechnical and Underground Space Engineering Xi'an710055China

As the profundity of open-pit mining operations has increased,so has the frequency of geological *** complex interaction of factors causing these disasters presents technical challenges for early warning and control ***,emergent technologies such as the internet,5G networks,and artificial intelligence provide new opportunities for constructing integrated digital early warning platforms that synthesise multifaceted monitoring data to predict and mitigate open-pit mine *** efficient Internet-mediated information integration,data from various sources can be consolidated for enhanced disaster *** paper reviews the current state of digital early warning platforms for open-pit mines using a Web of Science database search for pertinent *** framework,data layer,technology layer,and application layer of these platforms are investigated in order to identify associated technologies and *** results include:(1)Inconsistent data formats and monitoring software diminish platform workflow *** data exchange protocols and feature-rich software could increase efficiency.(2)Platforms rely on limited data types as opposed to intelligent algorithms that integrate diverse monitoring inputs into global disaster *** underutilization of advanced technologies such as artificial intelligence,the internet of things,and cloud *** calamity mechanisms and rock mechanics require additional study.

关键词： Digital early warning platform Geological disaster Data mining AI multi-source information fusion

Research on the Driver Fatigue Monitoring Method Based on the Dempster-Shafer Theory

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Research on the Driver Fatigue Monitoring Method Based on th...

A New Method of Analyzing Aquifer Water Abundance

2010 Chinese Control and Decision Conference

作者： Deng Sanpeng~(1,2),Xu Xiaoli~(1,3),Yang Xuecui~2,Miao Dehua~2 1.Department of Machine & Vehicle Engineering,Beijing Institute of Technology,Beijing 100080,China 2.Tianjin Key Laboratory of High Speed Cutting & Accurate Process Technology,Tianjin University of Technology and Education, Tianjin,300222,China 3.Beijing information Science & Technology University,Beijing,100192,China

＜正＞Fatigue driving is one of the most important factors leading to traffic ***’s of greatest significance to avoid fatigue driving and reduce traffic accidents by monitoring the state of drivers’ fatigue accurately and warning them of their fatigue.A fatigue assessment method based on the weighted Dempster-Shafer（D-S） theory has been proposed by using the detected multi-source information such as the image,human pulse and the steering wheel movement in an imperceptible way so as to enhance the fatigue monitoring accuracy and system robustness.

关键词： D-S evidence theory Driver fatigue multi-source information fusion

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A New Method of Analyzing Aquifer Water Abundance