2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)最新文献_第8页

Understanding the Fault in EMU Braking System 对动车组制动系统故障的认识

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213447

Xiuhua Tai, Tianxu Guo, Maoyin Chen, Junfeng Zhang, Donghua Zhou

引用次数: 0

Power Load Prediction Method Based on VMD and Dynamic Adjustment BP 基于VMD和动态调整BP的电力负荷预测方法

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213431

Fengtian Kuang, Darong Huang

{"title":"Power Load Prediction Method Based on VMD and Dynamic Adjustment BP","authors":"Fengtian Kuang, Darong Huang","doi":"10.1109/SAFEPROCESS45799.2019.9213431","DOIUrl":"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213431","url":null,"abstract":"Aiming at the shortcomings of low prediction accuracy due to the randomness and complexity of power load data, this paper bring up a power load prediction method on the strength of VMD and dynamic adjustment BP. Firstly, for the redundant information and trend components contained in the original data of the power load, the VMD decomposed component reconstruction is used to remove the trend component and the redundant information. Secondly, after the VMD detrended, there is a disadvantage that the fixed points in traditional BP neural network prediction may cause low accuracy, the dynamic adjustment of nodes is designed to achieve the optimal prediction. Finally, based on the electric load data provided by Chongqing Tongnan Electric Power Co., Ltd., the prediction model put forward in this paper is used to estimate the electric load. The comparison of the example simulation results shows that the predicted values of the VMD and the dynamically adjusted BP cooperative electric load forecasting method are closer to the real one. The load value and the prediction error are lower, which is a better short-term power load forecasting method.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133226286","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

Design of a Fault Diagnosis System for the “JiaoLong” Deep-sea Manned Vehicle “蛟龙”号深海载人潜航器故障诊断系统设计

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213307

Xu Chen, Xiao He

引用次数: 0

Thruster Fault Tolerant Control Scheme for 4500-m Human Occupied Vehicle 4500米载人车辆推力器容错控制方案

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213245

Ming Huang, Daqi Zhu, Zhenzhong Chu

引用次数: 0

Turnout Fault Diagnosis Algorithms of Full-Electronic Interlocking System Based on BP_Adaboost 基于BP_Adaboost的全电子联锁系统道岔故障诊断算法

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/safeprocess45799.2019.9213424

Guangwu Chen, Yijian Yu, Dongfeng Xing, Juhau Yang

{"title":"Turnout Fault Diagnosis Algorithms of Full-Electronic Interlocking System Based on BP_Adaboost","authors":"Guangwu Chen, Yijian Yu, Dongfeng Xing, Juhau Yang","doi":"10.1109/safeprocess45799.2019.9213424","DOIUrl":"https://doi.org/10.1109/safeprocess45799.2019.9213424","url":null,"abstract":"With the rapid development of Chinese railways, railway station signal control system has developed rapidly with the help of the fourth generation of all-electronic interlocking system. According to the control circuit and switching state in switch module of electronic interlocking system and monitor switching current, analysis the monitoring machine of turnout active current, the characteristic input value of turnout is extracted and turnout fault model is established. Firstly, data training and test is classified by BP neural network, then strong classifier is constructed by optimized Adaboost, the matching classification between turnout characteristic quantity and turnout fault type is carried out. After simulation, when BP neural network algorithm is used alone, the fault diagnosis rate is 90.2%, while the strong classification effect of BP_Adaboost algorithm can improve accuracy of turnout fault diagnosis by 95.8%, and the accuracy of latter is 5% higher than that of the former. The method validity is verified, which provides important research significance for turnout fault diagnosis of all-electronic interlocking system.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128640427","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

Learning Observer Based Fault Diagnosis and Fault Tolerant Control for Manipulators with Sensor Fault 基于学习观测器的传感器故障机器人故障诊断与容错控制

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213440

Wei Wu, Yunfeng Kang, L. Yao

引用次数: 2

Safety Assessment of the JiaoLong Deep-sea Manned Submersible based on Bayesian Network 基于贝叶斯网络的蛟龙号深海载人潜水器安全评价

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213370

Changli Liu, Yi Zhang, Xiao He

{"title":"Safety Assessment of the JiaoLong Deep-sea Manned Submersible based on Bayesian Network","authors":"Changli Liu, Yi Zhang, Xiao He","doi":"10.1109/SAFEPROCESS45799.2019.9213370","DOIUrl":"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213370","url":null,"abstract":"Safety assessment is of great importance to the deep-sea manned submersible, but little literature has been reported on this topic. The goal of this paper is to work out an effective tool for the safety assessment of the deep-sea manned submersible according to the study of JiaoLong, which is the first manned submersible that can dive more than 7,000 meters in China. In this paper, a relatively new subsystem division of the manned submersible is introduced firstly. Furthermore, a BN-based safety assessment method is proposed which combines the Bayesian Network (BN) and data-driven fault detection algorithms. Based on the BN, qualitative and quantitative analysis can both be implemented. Moreover, real-time safety assessment can be realized by combining data-driven fault detection algorithms. The proposed method is verified on the JiaoLong manned submersible by constructing and analyzing the BN. Also, an example of the propeller fault detection using kernel principal component analysis (KPCA) is displayed to illustrate how to employ the proposed method in real-time.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130325199","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

A New Control Allocation Method Based on the Improved Grey Wolf Optimizer Algorithm for Aircraft with Multiple Actuators 基于改进灰狼优化算法的多作动器飞机控制分配新方法

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213444

Wendong Gai, Chengxian Sun, Yecheng Zhou, Jing Zhang

引用次数: 0

A Fault Detection Method with Ensemble Empirical Mode Decomposition and Support Vector Data Description 基于集成经验模态分解和支持向量数据描述的故障检测方法

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213442

Yang Wang, D. Ling, Weidong Yang, Bo Tao, Ying Zheng

{"title":"A Fault Detection Method with Ensemble Empirical Mode Decomposition and Support Vector Data Description","authors":"Yang Wang, D. Ling, Weidong Yang, Bo Tao, Ying Zheng","doi":"10.1109/SAFEPROCESS45799.2019.9213442","DOIUrl":"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213442","url":null,"abstract":"In order for the fault detection of processes with noise and nonlinearity, a method based on Ensemble Empirical Mode Decomposition (EEMD) and Support Vector Data Description (SVDD) is proposed. In this work, EEMD-based denoising method is utilized to remove the noise from the original dataset. The SVDD model is then developed to handle the nonlinear data for fault detection. The proposed method contains three steps. Firstly, the original dataset is decomposed into a series of Intrinsic Mode Functions (IMFs) by the EEMD method. Each IMF characterizes the corresponding scale information of the data. Secondly, the original data is reconstructed using the partial reconstruction denoising method. Only the relevant IMFs which mostly contain useful information are retained, and the IMFs that primarily carry noise are discarded. The optimal number of relevant IMFs is selected based on the Signal-to-Noise Ratio (SNR). Finally, the SVDD model is constructed on the reconstructed data to detect faults. The effectiveness of the proposed method is demonstrated by a numerical example. The results show the proposed method performs better compared with other existing methods.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129098022","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

Fault Diagnosis of Chemical Processes Based on k-NN Distance Contribution Analysis Method 基于k-NN距离贡献分析法的化工过程故障诊断

2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) Pub Date : 2019-07-01 DOI: 10.1109/SAFEPROCESS45799.2019.9213324

Guo-Zhu Wang, Zhi-Yong Du, Yong-Tao Hu, Yuan Li

{"title":"Fault Diagnosis of Chemical Processes Based on k-NN Distance Contribution Analysis Method","authors":"Guo-Zhu Wang, Zhi-Yong Du, Yong-Tao Hu, Yuan Li","doi":"10.1109/SAFEPROCESS45799.2019.9213324","DOIUrl":"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213324","url":null,"abstract":"In modern chemical processes, varieties of fault detection and diagnosis methods have been used for ensuring process safety and product quality widely. As an important branch, fault detection and diagnosis methods based on data-driven are effective in large-scale chemical processes. However, they do not often show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian, and multi-operating mode. To cope with these issues, k-NN (k-Nearest Neighbor) fault detection method and its extension have been developed in recent years. Nevertheless, these methods are used for fault detection mainly, few papers can be found about fault diagnosis. In this paper, a novel abnormal variables identification method is proposed, this method uses k-NN distance contribution analysis theory to evaluate which variables are most likely to be abnormal, meanwhile, the feasibility of this method is verified by contribution decomposition theory. The proposed search strategy can guarantee that all abnormal variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126441040","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