[1]CUI Tiejun,LI Shasha.Study on entropy of system fault evolution process and its influence on logical relationships[J].CAAI Transactions on Intelligent Systems,2024,19(3):749-756.[doi:10.11992/tis.202209036]
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Study on entropy of system fault evolution process and its influence on logical relationships

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 749-756 Column: 学术论文—人工智能基础 Public date: 2024-05-05
Title:
Study on entropy of system fault evolution process and its influence on logical relationships
Author(s):
CUI Tiejun1; 2;  LI Shasha1; 3
1. School of Environmental and Chemical Engineering, Shenyang Ligong University, Shenyang 110159, China;
2. College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China;
3. School of Business Administration, Liaoni
Keywords:
safety system engineering; system fault evolution process; fault probability distribution; evolution entropy; distribution entropy; AND OR logic; influence research; system function status
CLC:
TP18；X913；C931.1
DOI:
10.11992/tis.202209036
Abstract:
To study the chaotic degree of system function state change in the system fault evolution process, the concept of entropy is introduced, and the entropy of the system fault evolution process (evolution entropy for short) is proposed. Evolution entropy is the chaotic degree of the system function state at a certain time in the evolution process. It is a measure of change in the chaotic degree of the system function state when the system evolution develops from one moment to another. It is numerically equal to the event fault probability distribution entropy at that moment (referred to as distribution entropy). The research shows that the evolution entropy can only be obtained from the event fault probability distribution at a certain moment and has a certain range of value domains. The logical relationship of events in the evolution process directly impacts the change in evolution entropy. The OR logic reduces the evolution entropy to less than the distribution entropy of any participating event. Moreover, the AND logic increases the evolution entropy to greater than the distribution entropy of any participating event. With the increase in transfer time, the effect of AND logic and OR logic on the increase and decrease in evolution entropy reduces, respectively. Finally, the determination process of evolution entropy is exhibited by an example, summarizing the problems existing in the current research and putting forward the following research direction.
References:
[1] 崔铁军, 李莎莎. 系统可靠-失效模型的哲学意义与智能实现[J]. 智能系统学报, 2020, 15(6): 1104–1112
CUI Tiejun, LI Shasha. Philosophical significance and implementation of an intelligent system based on the system reliability-failure model[J]. CAAI transactions on intelligent systems, 2020, 15(6): 1104–1112
[2] 崔铁军, 李莎莎. 系统故障演化过程的可拓学原理[J]. 广东工业大学学报, 2020, 37(5): 1–6
CUI Tiejun, LI Shasha. An extension theory of system fault evolution process[J]. Journal of Guangdong University of Technology, 2020, 37(5): 1–6
[3] 崔铁军. 空间故障网络理论与系统故障演化过程研究[J]. 安全与环境学报, 2020, 20(4): 1255–1262
CUI Tiejun. Profound trace and exploration into the space fault network theory and the system fault evolution process[J]. Journal of safety and environment, 2020, 20(4): 1255–1262
[4] 崔铁军. 系统故障演化过程描述方法研究[J]. 计算机应用研究, 2020, 37(10): 3006–3009
CUI Tiejun. Research on description method of system fault evolution process[J]. Application research of computers, 2020, 37(10): 3006–3009
[5] 魏东涛, 刘晓东, 周骏, 等. 基于DSM与信息熵的装备体系结构贡献率分析[J/OL]. 系统工程与电子技术, 2022: 1-9. (2022-03-16). https://kns.cnki.net/kcms/detail/11.2422.tn.20220314.1602.008.html.
WEI Dongtao, LIU Xiaodong, ZHOU Jun, et al. Evaluation method of equipment’s structure contribution rate to system-of-systems based on DSM and information entropy[J/OL]. Systems engineering and electronics, 2022: 1-9. (2022-03-16). https://kns.cnki.net/kcms/detail/11.2422.tn.20220314.1602.008.html.
[6] 刘雁, 李宇宸, 张赫, 等. 基于EWT的离心压气机出口动态压力样本熵分析[J]. 航空动力学报, 2023, 38(3): 655–664
LIU Yan, LI Yuchen, ZHANG He, et al. Sample entropy characteristics of dynamic pressure at outlet of centrifugal compressor based on EWT[J]. Journal of aerospace power, 2023, 38(3): 655–664
[7] 胡浩然, 陈树新, 吴昊, 等. 面向连续-离散系统的自适应最大相关熵滤波算法[J/OL]. 西安交通大学学报 2022(6): 1-9. (2022-03-08). https://kns.cnki.net/kcms/detail/61.1069.T.20220304.1843.006.html.
HU Haoran, CHEN Shuxin, WU Hao, et al. Adaptive maximum correntropy filtering algorithm for continuous-discrete systems[J/OL]. Journal of Xi’an Jiaotong University, 2022(6): 1-9. (2022-03-08). https://kns.cnki.net/kcms/detail/61.1069.T.20220304.1843.006.html.
[8] 陶洋, 祝小钧, 杨柳. 基于皮尔逊相关系数和信息熵的多传感器数据融合[J/OL]. 小型微型计算机系统, 2022: 1-7. (2022-03-01). https://kns.cnki.net/kcms/detail/21.1106.tp.20220225.1128.006.html.
TAO Yang, ZHU Xiaojun, YANG Liu. Multi-sensor data fusion based on Pearson coefficient and information entropy[J/OL]. Journal of Chinese computer systems, 2022: 1-7. (2022-03-01). https://kns.cnki.net/kcms/detail/21.1106.tp.20220225.1128.006.html.
[9] 伍文城, 王晓茹, 宋新立. 面向阻尼控制的交直流混合系统线性状态空间模型辨识[J/OL]. 电力系统自动化, 2022: 1-15. (2022-02-18) [ 2022-09-18]. https://kns.cnki.net/kcms/detail/32.1180.TP.20220217.1544.002.html.
WU Wencheng, WANG Xiaoru, SONG Xinli. Linear state space model identification of AC/DC hybrid system for damping control[J/OL]. Automation of electric power systems, 2022: 1-15. (2022-02-18)[ 2022-09-18]. https://kns.cnki.net/kcms/detail/32.1180.TP.20220217.1544.002.html.
[10] 陈思宏, 沈浩靖, 王冉, 等. 预测不确定性与对抗鲁棒性的关系研究[J]. 软件学报, 2022, 33(2): 524–538
CHEN Sihong, SHEN Haojing, WANG Ran, et al. Relationship between prediction uncertainty and adversarial robustness[J]. Journal of software, 2022, 33(2): 524–538
[11] 穆钢, 陈奇, 刘洪波, 等. 揭示电力系统运行数据中因果关系的逆信息熵因果推理方法[J/OL]. 中国电机工程学报, 2022: 1-14. (2022-01-27) [2022-09-18]. https://kns.cnki.net/kcms/detail/11.2107.TM.20220125.1748.007.html.
MU Gang, CHEN Qi, LIU Hongbo, et al. A reciprocal information entropy causal inference method for exploring the cause-effect relationship in power system operation data[J/OL]. Proceedings of the CSEE, 2022: 1-14. (2022-01-27) [2022-09-18]. https://kns.cnki.net/kcms/detail/11.2107.TM.20220125.1748.007.html.
[12] 郁彤彤, 王坚, 陈晓薇. 熵修正的混合人工蜂群-蝙蝠算法人群疏散模型[J]. 哈尔滨工业大学学报, 2021, 53(12): 80–88
YU Tongtong, WANG Jian, CHEN Xiaowei. Hybrid artificial bee colony-bat algorithm-based evacuation model with entropy correction[J]. Journal of Harbin institute of technology, 2021, 53(12): 80–88
[13] 李金海, 贺建君. 多粒度形式背景的不确定性度量与最优粒度选择[J]. 控制与决策, 2022, 37(5): 1299–1308
LI Jinhai, HE Jianjun. Uncertainty measurement and optimal granularity selection for multi-granularity formal context[J]. Control and decision, 2022, 37(5): 1299–1308
[14] 杨挺, 张璐, 张亚健, 等. 基于信息熵计算模型的电力信息物理系统融合控制方法[J]. 电力系统自动化, 2021, 45(12): 65–74
YANG Ting, ZHANG Lu, ZHANG Yajian, et al. Fusion control method for cyber-physical power system based on information entropy calculation model[J]. Automation of electric power systems, 2021, 45(12): 65–74
[15] 屠红艳, 张挺, 夏鹏飞, 等. 基于改进型变分自编码器的不确定性空间信息重建方法[J]. 计算机应用, 2021, 41(10): 2959–2963
TU Hongyan, ZHANG Ting, XIA Pengfei, et al. Reconstruction method for uncertain spatial information based on improved variational auto-encoder[J]. Journal of computer applications, 2021, 41(10): 2959–2963
[16] 吕萍, 常玉慧, 钱进. 知识粒度框架下不确定性度量研究[J]. 模糊系统与数学, 2020, 34(6): 140–149
LYU Ping, CHANG Yuhui, QIAN Jin. Research on uncertainty measures using knowledge granulation[J]. Fuzzy systems and mathematics, 2020, 34(6): 140–149
[17] 于本成, 丁世飞. 基于改进EMD算法的熵性能研究[J]. 郑州大学学报(理学版), 2018, 50(4): 39–44,49
YU Bencheng, DING Shifei. Research on entropy performance based on improved EMD algorithm[J]. Journal of Zhengzhou university (natural science edition), 2018, 50(4): 39–44,49
[18] HAYASHI T, CIMR D, FUJITA H, et al. Image entropy equalization: a novel preprocessing technique for image recognition tasks[J]. Information sciences, 2023, 647: 119539.
[19] TIAN Zhiqiang, LI Weigang, HU Junwei, et al. Joint graph entropy knowledge distillation for point cloud classification and robustness against corruptions[J]. Information sciences, 2023, 648: 119542.
[20] ZHANG Yucun, WU Zihao, LI Qun, et al. Research on point cloud simplification algorithm for ring forgings based on joint entropy evaluation[J]. Measurement science and technology, 2023, 34(12): 125203.
[21] DONG Delong, WANG Tianzhong, WANG Jinhui, et al. Study on tool wear state monitoring based on EEMD information entropy and PSO-SVM[J]. Journal of physics: conference series, 2023, 2566(1): 012111.
[22] XIONG Lijun, YUAN Haiping, LIU Gaoliang, et al. Synergetic theory of information entropy based on failure approach index for stability analysis of surrounding rock system[J]. Entropy, 2023, 25(8): 1237.
[23] DENG Hongxing, WEN Wen, ZHOU Jie. Competitiveness evaluation of express delivery enterprises based on the information entropy and gray correlation analysis[J]. Sustainability, 2023, 15(16): 12469.
[24] 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
[25] 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 Teijun, MA Yundong. Research on multi-dimensional space fault tree construction and application[J]. China safety science journal, 2013, 23(4): 32–37,62
[26] 何华灿. 重新找回人工智能的可解释性[J]. 智能系统学报, 2019, 14(3): 393–412
HE Huacan. Refining the interpretability of artificial intelligence[J]. CAAI transactions on intelligent systems, 2019, 14(3): 393–412
[27] 何华灿. 泛逻辑学理论: 机制主义人工智能理论的逻辑基础[J]. 智能系统学报, 2018, 13(1): 19–36
HE Huacan. Universal logic theory: logical foundation of mechanism-based artificial intelligence theory[J]. CAAI transactions on intelligent systems, 2018, 13(1): 19–36
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