[1]崔铁军,李莎莎.线性熵的系统故障熵模型及其时变研究[J].智能系统学报,2021,16(6):1136-1142.[doi:10.11992/tis.202006034]
 CUI Tiejun,LI Shasha.System fault entropy model and its time-varying based on linear entropy[J].CAAI Transactions on Intelligent Systems,2021,16(6):1136-1142.[doi:10.11992/tis.202006034]
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线性熵的系统故障熵模型及其时变研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年6期
页码:
1136-1142
栏目:
学术论文—人工智能基础
出版日期:
2021-11-05

文章信息/Info

Title:
System fault entropy model and its time-varying based on linear entropy
作者:
崔铁军1 李莎莎2
1. 辽宁工程技术大学 安全科学与工程学院,辽宁 阜新 123000;
2. 辽宁工程技术大学 工商管理学院,辽宁 葫芦岛 125105
Author(s):
CUI Tiejun1 LI Shasha2
1. College of Safety Science and Engineering, Liaoning Technical University, Fuxin 123000, China;
2. School of Business Administration, Liaoning Technical University, Huludao 125105, China
关键词:
智能科学安全科学安全系统工程空间故障树因素空间系统故障熵线性熵时变分析
Keywords:
intelligent sciencesafety sciencesafety system engineeringspace fault treefactor spacesystem fault entropylinear entropytime-varying analysis
分类号:
TP18;X913;C931.1
DOI:
10.11992/tis.202006034
摘要:
为研究系统故障在不同因素叠加时体现的总体规律、故障变化程度和故障信息量,提出系统故障熵的概念。基于线性熵的线性均匀度特性,推导了多因素相被划分为两状态时的线性熵模型。认为线性熵可以表征系统故障熵,进而研究了系统故障熵的时变特征。对连续时间间隔内的不同因素状态叠加下系统故障进行统计,得到系统故障概率分布,绘制系统故障熵时变曲线。从结果来看至少可以完成3项任务:从变化规律得到考虑不同因素影响下的系统故障熵变化情况,系统故障熵的总体变化规律,系统可靠性的稳定性。此研究可应用于类似情况下的各领域故障及数据分析。
Abstract:
The concept of system fault entropy is proposed to study the general rule, fault variation degree, and the fault information of the system fault under different superposed factors. A linear entropy model with a multifactor phase divided into two states is derived based on the linear uniformity of the linear entropy. The linear entropy can represent the system fault entropy; thus, the time-varying characteristics of the system fault entropy can be studied. The system fault probability distribution is obtained by counting the faults under the superposition of different factor states in continuous time intervals. Then, the time-varying curve of the system fault entropy is drawn. The results show that at least three tasks can be completed: 1) Obtaining the change of the system fault entropy under different factors from the change law. 2) Obtaining the general change rule of the system fault entropy. 3) Studying the stability of the system reliability. The research results can be applied to fault and data analyses in various fields in similar cases.

参考文献/References:

[1] 崔铁军, 马云东. 多维空间故障树构建及应用研究[J]. 中国安全科学学报, 2013, 23(4): 32-37, 62
CUI Tiejun, MA Yundong. Research on multi-dimensional space fault tree construction and application[J]. China safety science journal, 2013, 23(4): 32-37, 62
[2] 刘炜, 李思文, 王竞, 等. 基于EWT能量熵的直流短路故障辨识[J]. 电力自动化设备, 2020, 40(2): 149-153
LIU Wei, LI Siwen, WANG Jing, et al. Identification of DC short circuit fault based on EWT energy entropy[J]. Electric power automation equipment, 2020, 40(2): 149-153
[3] 杨洪涛. 样本熵改进小波包阈值去噪的轴承故障诊断[J]. 组合机床与自动化加工技术, 2020(1): 79-82, 88
YANG Hongtao. Bearing fault diagnosis based on wavelet packet threshold de-noise algorithm improved by sample entropy[J]. Modular machine tool & automatic manufacturing technique, 2020(1): 79-82, 88
[4] 刘渝根, 陈超, 杨蕊菁, 等. 基于小波相对熵的变电站直流系统接地故障定位方法[J]. 高压电器, 2020, 56(1): 169-174
LIU Yugen, CHEN Chao, YANG Ruijing, et al. Location method of ground fault in DC system of substation based on wavelet relative entropy[J]. High voltage apparatus, 2020, 56(1): 169-174
[5] 李永健, 宋浩, 刘吉华, 等. 基于改进多尺度排列熵的列车轴箱轴承诊断方法研究[J]. 铁道学报, 2020, 42(1): 33-39
LI Yongjian, SONG Hao, LIU Jihua, et al. A study on fault diagnosis method for train axle box bearing based on modified multiscale permutation entropy[J]. Journal of the China railway society, 2020, 42(1): 33-39
[6] 张龙, 吴荣真, 雷兵, 等. 基于多尺度熵的滚动轴承故障可拓智能识别[J]. 噪声与振动控制, 2019, 39(6): 200-205
ZHANG Long, WU Rongzhen, LEI Bing, et al. Extensible intelligent identification for rolling bearing faults using multiscale entropy[J]. Noise and vibration control, 2019, 39(6): 200-205
[7] 张雅丽, 刘永姜, 张航, 等. 基于ITD信息熵与PNN的轴承故障诊断[J]. 煤矿机械, 2019, 40(12): 167-169
ZHANG Yali, LIU Yongjiang, ZHANG Hang, et al. Bearing fault diagnosis based on ITD information entropy and PNN[J]. Coal mine machinery, 2019, 40(12): 167-169
[8] 赵书涛, 李云鹏, 王二旭, 等. 基于电—振信号熵权特征的断路器储能机构故障诊断方法[J]. 高压电器, 2019, 55(11): 204-210
ZHAO Shutao, LI Yunpeng, WANG Erxu, et al. Fault diagnosis method of circuit breaker energy storage mechanism based on electro-vibration signal entropy weight feature[J]. High voltage apparatus, 2019, 55(11): 204-210
[9] 张国辉, 冯俊栋, 徐丙立, 等. 基于故障特征信息量的诊断策略优化仿真研究[J]. 计算机仿真, 2019, 36(11): 317-321
ZHANG Guohui, FENG Jundong, XU Bingli, et al. Research on DMFT test method based on hybrid diagnostic model[J]. Computer simulation, 2019, 36(11): 317-321
[10] 戴邵武, 陈强强, 戴洪德, 等. 基于平滑先验分析和模糊熵的滚动轴承故障诊断[J]. 航空动力学报, 2019, 34(10): 2218-2226
DAI Shaowu, CHEN Qiangqiang, DAI Hongde, et al. Rolling bearing fault diagnosis based on smoothness priors approach and fuzzy entropy[J]. Journal of aerospace power, 2019, 34(10): 2218-2226
[11] 王志, 李有儒, 田晶, 等. 基于EMD模糊熵与会诊决策融合模型的中介轴承故障诊断技术[J]. 航空发动机, 2019, 45(5): 76-81
WANG Zhi, LI Youru, TIAN Jing, et al. Fault diagnosis technology of inter-shaft bearing based on EMD fuzzy entropy and consultative decision fusion model[J]. Aeroengine, 2019, 45(5): 76-81
[12] 赵小强, 张和慧. 基于交叉熵的改进NPE间歇过程故障检测算法[J]. 控制与决策, 2021, 36(2): 411-417
ZHAO Xiaoqiang, ZHANG Hehui. Improved NPE batch process fault detection algorithm based on cross entropy[J]. Control and decision, 2021, 36(2): 411-417
[13] KUMAR A, GANDHI C P, ZHOU Yuqing, et al. Fault diagnosis of rolling element bearing based on symmetric cross entropy of neutrosophic sets[J]. Measurement, 2020, 152: 107318.
[14] MINHAS A S, SINGH G, SINGH J, et al. A novel method to classify bearing faults by integrating standard deviation to refined composite multi-scale fuzzy entropy[J]. Measurement, 2020, 154: 107441.
[15] TRUONG M T N, KIM S. Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection[J]. Soft computing, 2018, 22(13): 4197-4203.
[16] MINHAS A S, SINGH S, MALHOTRA J, et al. Machine deterioration identification for multiple nature of faults based on autoregressive-approximate entropy approach[J]. Life cycle reliability and safety engineering, 2018, 7(3): 185-192.
[17] 刘天寿, 匡海波, 刘家国, 等. 区间数熵权TOPSIS的港口安全管理成熟度评价[J]. 哈尔滨工程大学学报, 2019, 40(5): 1024-1030
LIU Tianshou, KUANG Haibo, LIU Jiaguo, et al. Evaluation on maturity of port safety management based on interval entropy weight TOPSIS[J]. Journal of Harbin Engineering University, 2019, 40(5): 1024-1030
[18] 杜鑫, 邱庆刚, 丁雅倩, 等. 超临界水冷堆子通道中熵产行为数值研究[J]. 哈尔滨工程大学学报, 2018, 39(8): 1290-1295
DU Xin, QIU Qinggang, DING Yaqian, et al. Numerical research on entropy generation in a sub-channel of SCWR[J]. Journal of Harbin Engineering University, 2018, 39(8): 1290-1295
[19] 赵宏伟, 王也然, 刘萍萍, 等. 利用位置信息熵改进VLAD的图像检索方法[J]. 哈尔滨工程大学学报, 2018, 39(8): 1376-1381
ZHAO Hongwei, WANG Yeran, LIU Pingping, et al. Improved VLAD using location information entropy in image retrieval[J]. Journal of Harbin Engineering University, 2018, 39(8): 1376-1381
[20] 崔铁军, 马云东. 基于多维空间事故树的维持系统可靠性方法研究[J]. 系统科学与数学, 2014, 34(6): 682-692
CUI Tiejun, MA Yundong. Research on the maintenance method of system reliability based on multi-dimensional space fault tree[J]. Journal of systems science and mathematical sciences, 2014, 34(6): 682-692
[21] 崔铁军, 马云东. 基于SFT理论的系统可靠性评估方法改造研究[J]. 模糊系统与数学, 2015, 29(5): 173-182
CUI Tiejun, MA Yundong. Reliability assessment method based on space fault tree[J]. Fuzzy systems and mathematics, 2015, 29(5): 173-182
[22] 崔铁军, 马云东. DSFT的建立及故障概率空间分布的确定[J]. 系统工程理论与实践, 2016, 36(4): 1081-1088
CUI Tiejun, MA Yundong. Discrete space fault tree construction and failure probability space distribution determination[J]. Systems engineering-theory & practice, 2016, 36(4): 1081-1088
[23] 崔铁军, 马云东. DSFT中因素投影拟合法的不精确原因分析[J]. 系统工程理论与实践, 2016, 36(5): 1340-1345
CUI Tiejun, MA Yundong. Inaccurate reason analysis of the factors projection fitting method in DSFT[J]. Systems engineering-theory & practice, 2016, 36(5): 1340-1345
[24] 崔铁军, 汪培庄, 马云东. 01SFT中的系统因素结构反分析方法研究[J]. 系统工程理论与实践, 2016, 36(8): 2152-2160
CUI Tiejun, WANG Peizhuang, MA Yundong. Inward analysis of system factor structure in 01 space fault tree[J]. Systems engineering-theory & practice, 2016, 36(8): 2152-2160
[25] 崔铁军, 马云东. 基于因素空间的煤矿安全情况区分方法的研究[J]. 系统工程理论与实践, 2015, 35(11): 2891-2897
CUI Tiejun, MA Yundong. Research on the classification method about coal mine safety situation based on the factor space[J]. Systems engineering-theory & practice, 2015, 35(11): 2891-2897
[26] 崔铁军, 马云东. 因素空间的属性圆定义及其在对象分类中的应用[J]. 计算机工程与科学, 2015, 37(11): 2169-2174
CUI Tiejun, MA Yundong. Definition of attribute circle in factor space and its application in object classification[J]. Computer engineering & science, 2015, 37(11): 2169-2174
[27] 崔铁军, 汪培庄. 空间故障树与因素空间融合的智能可靠性分析方法[J]. 智能系统学报, 2019, 14(5): 853-864
CUI Tiejun, WANG Peizhuang. Intelligent reliability analysis method based on space fault tree and factor space[J]. CAAI transactions on intelligent systems, 2019, 14(5): 853-864
[28] 崔铁军, 李莎莎, 朱宝艳. 含有单向环的多向环网络结构及其故障概率计算[J]. 中国安全科学学报, 2018, 28(7): 19-24
CUI Tiejun, LI Shasha, ZHU Baoyan. Multidirectional ring network structure with one-way ring and its fault probability calculation[J]. China safety science journal, 2018, 28(7): 19-24
[29] CUI Tiejun, LI Shasha. Research on complex structures in space fault network for fault data mining in system fault evolution process[J]. IEEE access, 2019, 7: 121881-121896.
[30] 崔铁军, 李莎莎. 安全科学中的故障信息转换定律[J]. 智能系统学报, 2020, 15(2): 360-366
CUI Tiejun, LI Shasha. Conversion law of fault information in safety science[J]. CAAI transactions on intelligent systems, 2020, 15(2): 360-366
[31] CUI Tiejun, LI Shasha. System movement space and system mapping theory for reliability of IoT[J]. Future generation computer systems, 2020, 107: 70-81.
[32] 安敬民, 李冠宇. 基于最小信息熵分类的不确定元数据本体构建[J]. 计算机工程与设计, 2018, 39(9): 2758-2763
AN Jingmin, LI Guanyu. MIE-categorized uncertain metadata ontology construction[J]. Computer engineering and design, 2018, 39(9): 2758-2763
[33] 汪培庄. 因素空间与人工智能[J]. 中国人工智能学会通讯, 2020, 10(1): 15-21
WANG Peizhuang. Factor space and artificial intelligence[J]. Communication of China artificial intelligence society, 2020, 10(1): 15-21

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备注/Memo

备注/Memo:
收稿日期:2020-06-21。
基金项目:国家自然科学基金项目(52004120,51704141);国家重点研发计划重点专项(2017YFC1503102);国家自然科学基金委主任基金项目(61350003)
作者简介:崔铁军,副教授,博士,主要研究方向为系统可靠性及力学系统稳定性。提出和建立了空间故障树及空间故障网络理论。主持国家自然科学基金项目1项。获得多项优秀论文奖。获发明专利授权22项,出版学术专著4部,发表学术论文100余篇;李莎莎,讲师,博士,主要研究方向为安全管理及其智能分析。参加了因素空间和空间故障树理论的研究。主持国家自然科学基金项目1项。获发明专利授权5项,出版学术专著2部,发表学术论文20余篇
通讯作者:崔铁军.E-mail:ctj.159@163.com
更新日期/Last Update: 2021-12-25