2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)最新文献

Cycle life prediction of lithium ion battery based on DE-BP neural network 基于DE-BP神经网络的锂离子电池循环寿命预测

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00033

Zhao Yao, Shun Lu, Yingshun Li, X. Yi

引用次数: 3

Establishment of corrosion big data standard acquisition platform for refining process 建立精炼过程腐蚀大数据标准采集平台

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00183

Liangchao Chen, Jianfeng Yang, Guanghai Li, Xin-yuan Lu

{"title":"Establishment of corrosion big data standard acquisition platform for refining process","authors":"Liangchao Chen, Jianfeng Yang, Guanghai Li, Xin-yuan Lu","doi":"10.1109/SDPC.2019.00183","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00183","url":null,"abstract":"The advent of the internet of things and big data era has brought new ideas for corrosion analysis and prediction of refining units. With the establishment and application of all kinds of information systems, refining enterprises have accumulated a large number of structured, unstructured, corrosion influencing factors and corrosion result data sources, but all kinds of system data are independent, and the traditional storage methods are limited in calculation, processing and analysis and mining ability, which do not have the conditions of big data analysis and utilization, so it is urgent to study the standardized collection method of corrosion data and establish a unified data center. Based on the whole life process of equipment, this paper studies the data content, data characteristics and current main data acquisition and management forms of corrosion related data in each link of the equipment through-life. In addition, this paper puts forward the overall structure of corrosion big data standard acquisition system for refining process in the enterprise as a whole. The big data acquisition system, which is suitable for corrosion big data comprehensive collection, efficient storage and open interface, is established to carry on the data acquisition step by step, and lays a foundation for the acquisition and analysis and utilization of corrosion big data from refining process.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121798843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reliability Modeling and Analysis of Command and Control Network Based on Dynamic Bayes-GO Method 基于动态Bayes-GO方法的指挥控制网络可靠性建模与分析

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00111

H. Mu, Hong-Mei Yan, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen

{"title":"Reliability Modeling and Analysis of Command and Control Network Based on Dynamic Bayes-GO Method","authors":"H. Mu, Hong-Mei Yan, X. Yi, Yuan-Yuan Yang, Guang-Liang Chen","doi":"10.1109/SDPC.2019.00111","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00111","url":null,"abstract":"A The combat command and control system is the nerve center to carry out joint operations. It is an important part of military operations in the information age and plays the role of \"amplification multiplier\" in the enemy operations. In this paper, a combat command network system was taken as the research object, and the reliability modeling and analysis of the combat command network were conducted based on dynamic Bayes-GO method. Firstly, the mapping rules for transforming common operators, type 18A and type 20 operators in GO method into dynamic Bayesian networks are given. Then, the task of the brigade command vehicle 2 commanding the combat vehicle 5 to strike the target in a combat command network was modeled and analyzed. Finally, the dynamic availability curve of the system within t = 300h and the availability corresponding to the system at each moment are calculated, which can enable the combat commander to clearly judge the reliability of the system in successfully hitting the target at each moment, thus the other combat equipment can be better dispatched for cooperative operations.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121065454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability design optimization method based on information reconstruction Kriging model 基于信息重构Kriging模型的可靠性设计优化方法

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00202

Meng Qin, Hairui Zhang, Guofeng Zhou, Hongya Wang, Cheng Zhang, Peihao He

{"title":"Reliability design optimization method based on information reconstruction Kriging model","authors":"Meng Qin, Hairui Zhang, Guofeng Zhou, Hongya Wang, Cheng Zhang, Peihao He","doi":"10.1109/SDPC.2019.00202","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00202","url":null,"abstract":"An efficient reliability design optimization method based on information reconstruction Kriging model is developed to further improve the computational efficiency. Inspired by the concept of incremental shifting vector, the conventional nested double-level optimization can be decomposed into updated deterministic optimizations and reliability analysis subproblems, which can simplify the reliability design optimization problem. The history iteration information of the reliability analysis is used to reconstruct the Kriging model and the active Kriging method is employed to address the reliability analysis problems efficiently. The most probable points (MPP) and its gradients of the current iteration process for the reliability constraints are obtained approximately to update the deterministic optimizations. Two numerical examples are investigated to demonstrate the effectiveness and efficiency of the proposed method. It is shown that the proposed method can improve the calculation efficiency while satisfying precision. And the method has the characteristics of high precision and moderate calculation when dealing with nonlinear problems.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127088096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An intelligent fault diagnosis method for a diesel engine valve based on improved wavelet packet-Mel frequency and convolutional neural network 基于改进小波包mel频率和卷积神经网络的柴油机气门智能故障诊断方法

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00071

Haipeng Zhao, Zhiwei Mao, Kun Chen, Zhinong Jiang

{"title":"An intelligent fault diagnosis method for a diesel engine valve based on improved wavelet packet-Mel frequency and convolutional neural network","authors":"Haipeng Zhao, Zhiwei Mao, Kun Chen, Zhinong Jiang","doi":"10.1109/SDPC.2019.00071","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00071","url":null,"abstract":"Considering the diesel engine vibration signals have the characteristics of the non-stability and non-linearity due to its compact-complex structure, strong noise and especially unstable operating conditions, we proposes an novel method based on improved wavelet packet-Mel frequency and convolutional neural network (CNN) to extract features and diagnose faults of diesel engine valve. Firstly, the wavelet packet transform is applied with the purpose of decomposing vibration signal and reconstructing each wavelet packet coefficient. Secondly, an improved Mel frequency cepstrum method is used to extract features from the reconstructed vibration signals. MFC algorithm is a well-known feature extraction technique widely used for speech recognition. Then, feature matrixes are constituted to obtain more definite and comprehensive time-frequency distributed representation, of which the row represents the average Mel frequency cepstrum coefficients and the column represents the frequency bands of wavelet packet decomposition in ascending order. Finally, a deep hierarchical CNN structure constructed by convolution layers, max-pooling layers and fully-connected layers is trained using a standard backpropagation, of which the input of first layer with 256 neurons is the above 2D feature matrixes and the output of final layer with 3 neurons is the number of vibration signal states. The experimental results of the fault diagnosis for the diesel engine valves show that the proposed method has the good diagnosis performance for diesel engine valve clearance faults.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123697904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Restoration of Underwater Polarized Images Based on DCP 基于DCP的水下偏振图像复原

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00127

Aiping Tang

引用次数: 3

Study on Perceptive Fuzzy Petri Net-based Autoloader Fault Analysis 基于感知模糊Petri网的自动装弹机故障分析研究

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00096

Yingshun Li, S. Sheng, Yintu Zhang, X. Yi

{"title":"Study on Perceptive Fuzzy Petri Net-based Autoloader Fault Analysis","authors":"Yingshun Li, S. Sheng, Yintu Zhang, X. Yi","doi":"10.1109/SDPC.2019.00096","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00096","url":null,"abstract":"To address the problems of high incidence of faults in tank autoloaders, long diagnosis cycle and low accuracy of diagnosis, this paper proposed a perceptive fuzzy Petri net-based fault diagnosis method on the basis of relevant expertise. The corresponding NFPN failure model was established according to the specific structure of the autoloader, fuzzy Petri net was used to present the process of fault propagation, the perceptron error back propagation method was adopted to learn the limited expertise, and the values of arc weights of trigger accidents in the Petri net were determined. An accurate judgment on autoloader faults was achieved by way of forwarding reasoning. At the time of backward reasoning, the minimal cut set method was also adopted to narrow the troubleshooting scope, thus improving the reasoning efficiency. By taking an autoloader with a rotary failure as an example, this paper established the corresponding PFPN fault model and made a comparison with the fault tree seasoning method and the historical statistic data. The comparison results reveal that this method can realize a quick and high-efficiency fault diagnosis of autoloaders thanks to its higher reliability and accuracy compared with the traditional fault tree diagnosis method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122060497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Suitability Evaluation ofLTE-based Wireless Private Network for Power Communication Business in Smart Grid 基于lte的无线专网在智能电网电力通信业务中的适用性评价

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00133

Yajuan Guo, Daohua Zhu, Lei Wei, Hongfu Guo, Shuqiong Zhu, Lei Feng

引用次数: 0

Automatic Color Recognition Technology of UAV Based on Machine Vision 基于机器视觉的无人机颜色自动识别技术

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00047

Guanghui Liu, Chunlin Zhang, Qing Guo, Fangyi Wan

{"title":"Automatic Color Recognition Technology of UAV Based on Machine Vision","authors":"Guanghui Liu, Chunlin Zhang, Qing Guo, Fangyi Wan","doi":"10.1109/SDPC.2019.00047","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00047","url":null,"abstract":"With the rapid development of artificial intelligence technology, machine vision, as a branch of it, has been widely used in various fields. Among them, color recognition is an important application in machine vision. Based on the background of the international UAV innovation competition, this paper studies the real-time color direction light image collected by the UAV camera, and proposes an automatic color recognition model of UAV based on machine vision. In this paper, the characteristics of the directional signal light image acquired by UAV under different color space models and its influence on the color recognition effect are expounded, and the principle and conversion method of the HSV color space model are emphatically discussed. Through image edge detection, erosion and dilation and closed operation processing, the geometric features of the image of the directional signal light are extracted, thereby identifying the color of the directional signal light. Finally, the experiment of automatic recognition of the color of the airport signal lights shows that the proposed color recognition model can effectively detect the objects in complex background.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimization and simulation of MEMS safety and arming device for the fuze 引信微机电系统安全解除装置的优化与仿真

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) Pub Date : 2019-08-01 DOI: 10.1109/SDPC.2019.00067

Qichen Han, Xiaojun Wu, Jinwei Yang, Xiaojin Chen, Junhu Zhang, Weizhao Chao, Hua Guo

引用次数: 0