Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 601-628.doi: 10.13229/j.cnki.jdxbgxb20221370

   

Speculative views on health assessment of complex systems

Quan QUAN1(),Gen CUI1,Zhi-yao ZHAO2,Xun-hua DAI3,Chang WEN4,Kai-yuan CAI1   

  1. 1.School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China
    2.School of Artificial Intelligence，Beijing Technology and Business University，Beijing 100048，China
    3.School of Computer Science and Engineering，Central South University，Changsha 410083，China
    4.Beihang University Hospital，Beijing 100191，China
  • Received:2022-10-26 Online:2023-03-01 Published:2023-03-29

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Abstract:

With the increasing system complexity in various engineering applications， greater demands are being placed on the reliability and safety of these systems. A kind of more comprehensive requirement called “health” has been naturally put up beyond reliability and safety. Consequently， Prognostics and Health Management （PHM） for complex systems has naturally become a hotspot in systems engineering. Moreover， it has been applied in many areas， such as aerospace， machinery， and power electronics. This paper summarizes and proposes a framework for the health assessment of complex systems， which consists of four aspects： data acquisition， data processing， health assessment， and health prediction. At last， the prospect view of health assessment is presented， and a system-leveled open-source project， namely OpenHA （Open Health Assessment） is suggested.

Key words: system engineering, complex system, health assessment, health prediction, OpenHA

CLC Number: 

  • TP277

Fig.1

Line plot of the number of PHM-related iteratures in each year"

Fig.2

Clustering analysis results from CiteSpace"

Fig.3

Relationship between faults and symptoms"

Fig.4

Condition/performance degradation or deterioration curves over time"

Fig.5

Flow chart of health assessment"

Fig.6

General architecture for health assessment"

Fig.7

General procedure of data processing"

Fig.8

Denoising methods by signal decomposition"

Table 1

Common statistic characteristics ofdigital signal"

序号特征名称计算公式
1均值（Mean）μ=1n∑i=1nxi
2整理平均值（Average rectified value）XARV=1n∑n=1nxi
3方差 （Variance）σ2=1n-1∑i=1nxi-μ2
4方根幅值Xr=1n∑i=1nxi2
5峰峰值（Peak to peak）XPP=maxxi-minxi
6峰值（Peak）XP=maxxi
7均方根（Root mean square，RMS）XRMS=1n∑i=1nxi2
8峰度（Kurtosis）XK=1σ4∑i=1nxi-μ4n

Fig.9

Schematic diagram of principal component analysis"

Fig.10

Schematic diagram of convolutionalneural network"

Fig.11

Schematic diagram of membership function"

Fig.12

Healthy and unhealthy sets under fuzzy logic"

Fig.13

Schematic diagram of healthy or unhealthy sets"

Fig.14

Step-by-step attribute assessment flow chart"

Table 2

Table for determining the weight condition"

体重情况BMI
体重过低<18.5
体重正常18.5~23.9
超重24~27.9
肥胖≥28

Fig.15

Schematic diagram of assessment method based on output bias"

Fig.16

Schematic diagram of assessment methodbased on state deviations"

Fig.17

Schematic diagram of clustering"

Fig.18

Total health of complex systems depends on all subsystems， sensors and components， etc."

Fig.19

Schematic diagram of different structures and configurations of different multicopters"

Fig.20

Common reliability block diagrams"

Fig.21

Schematic diagram of fault tree"

Fig.22

Flow chart and a simple example of phase-cyclic system"

Fig.23

Schematic diagram of state prediction"

Fig.24

Schematic diagram of model fitting and prediction"

Fig.25

Monte Carlo methodology"

1 Laprie J C, Medhaffer-Kanoun K. Dependability modeling of safety systems[J]. Microelectronics Reliability, 1982, 22(5): 997-1026.
2 Yan Y X, Liu Y Q, Li M, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese[J]. Journal of Epidemiology, 2009, 19(6): 333-341.
3 Cai K Y. System failure engineering and fuzzy methodology an introductory overview[J]. Fuzzy Sets and Systems, 1996, 83(2): 113-133.
4 Vichare N M, Pecht M G. Prognostics and health management of electronics[J]. IEEE Transactions on Components and Packaging Technologies, 2006, 29(1): 222-229.
5 Aaseng G B, Patterson-Hine A, Garcia-Galan C. A review of system health state determination methods[C]∥1st Space Exploration Conference: Continuing the Voyage of Discovery, Orlando, Florida, USA, 2005: No.2005-2528.
6 Ranasinghe K, Sabatini R, Gardi A, et al. Advances in integrated system health management for mission-essential and safety-critical aero-space applications[J]. Progress in Aerospace Sciences, 2022, 128: No.100758.
7 Annamdas V G M, Bhalla S, Soh C K. Applications of structural health monitoring technology in Asia[J]. Structural Health Monitoring, 2017, 16(3): 324-346.
8 Tibaduiza Burgos D A, Gomez Vargas R C, et al. Damage identification in structural health monitoring: a brief review from its implementation to the use of data-driven applications[J]. Sensors, 2020, 20(3), No.733.
9 尚德广, 夏禹, 薛龙, 等. 飞机结构单机疲劳寿命监控技术研究综述[J]. 北京工业大学学报, 2020, 46(6): 556-570.
Shang De-guang, Xia Yu, Xue Long, et al. Review on fatigue life monitoring technology for individual aircraft structure[J]. Journal of Beijing University of Technology, 2020, 46(6): 556-570.
10 张卫方, 何晶靖, 阳劲松, 等. 面向飞行器结构的健康监控技术研究现状[J]. 航空制造技术, 2017(19): 38-47.
Zhang Wei-fang, He Jing-jing, Yang Jin-song, et al. Research status on structural health monitoring technology for aircraft structures[J]. Aeronautical Manufacturing Technology, 2017(19): 38-47.
11 Smith G, Schroeder J B, Navarro S, et al. Development of a prognostics and health management capability for the joint strike fighter[C]∥1997 IEEE Autotestcon Proceedings, Anaheim, CA, USA, 1997: 676­682.
12 Chen C. CiteSpace: a Practical Guide for Mapping Scientific Literature[M].Hauppauge, NY, USA: Nova Science Publishers, 2016.
13 周圣林. OSA-CBM 标准适用性分析和航空应用探讨[J]. 航空标准化与质量, 2012(3): 38-41.
Zhou Sheng-lin. Analysis of the applicability of the OSA-CBM standard and exploration of aviation applications[J]. Aeronautic Standardization & Quality, 2012(3): 38-41.
14 Sheppard J W, Kaufman M A, Wilmer T J. IEEE standards for prognostics and health management[J]. IEEE Aerospace and Electronic Systems Magazine, 2009, 24(9): 34-41.
15 Jouin M, Gouriveau R, Hissel D, et al. Prognostics and health management of PEMFC-State of the art and remaining challenges[J]. International Journal of Hydrogen Energy, 2013, 38(35): 15307-15317.
16 Ladyman J, Lambert J, Wiesner K. What is a complex system?[J]. European Journal for Philosophy of Science, 2013, 3(1): 33-67.
17 赵峙尧. 基于率模可靠度的一类混杂动态系统健康评估技术[D]. 北京: 北京航空航天大学自动化科学与电气工程学院, 2017.
Zhao Zhi-yao. A profust reliability based health evaluation technique for a class of hybrid dynamical systems[D]. Beijing: School of Automation Science and Electrical Engineering, Beihang University, 2017.
18 Wang D, Tsui K L, Miao Q. Prognostics and health management: a review of vibration based bearing and gear health indicators[J]. IEEE Access, 2018, 6: 665-676.
19 彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J]. 仪器仪表学报, 2014, 35(3): 481-495.
Peng Yu, Liu Da-tong. Data-driven prognostics and health management: a review of recent advances[J]. Chinese Journal of Scientific Instrument, 2014, 35(3): 481-495.
20 景博, 徐光跃, 黄以锋, 等. 军用飞机PHM技术进展分析及问题研究[J]. 电子测量与仪器学报, 2017, 31(2): 161-169.
Jing Bo, Xu Guang-yue, Huang Yi-feng, et al. Recent advances analysis and new problems research on PHM technology of military aircraft[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(2): 161-169.
21 Esperon-Miguez M, John P, Jennions I K. A review of integrated vehicle health management tools for legacy platforms: challenges and opportunities[J]. Progress in Aerospace Sciences, 2013, 56: 19-34.
22 Dai X, Ke C, Quan Q, et al. RflySim: automatic test platform for UAV autopilot systems with FPGA-based hardware-in-the-loop simulations[J]. Aerospace Science and Technology, 2021, 114: No.106727.
23 Dai X, Ke C, Quan Q, et al. Simulation credibility assessment methodology with FPGA-based hardware-in-the-loop platform[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3282-3291.
24 邱静, 刘冠军, 杨鹏, 等. 装备测试性建模与设计技术[M]. 北京: 科学出版社, 2012.
25 石君友. 测试性试验与评价[M]. 北京:北京航空航天大学出版社, 2021.
26 张永芳, 王霞, 邢志国, 等. 面向机械装备健康监测的振动传感器研究现状[J]. 材料导报, 2020, 34(13): 13121-13130.
Zhang Yong-fang, Wang Xia, Xing Zhi-guo, et al. Research on vibration sensors for health monitoring of mechanical equipment[J]. Materials Reports, 2020, 34(13): 13121-13130.
27 Araujo A, Garcia-Palacios J, Blesa J, et al. Wireless measurement system for structural health monitoring with high time-synchronization accuracy[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(3): 801-810.
28 乔宁国. 基于多传感器数据融合的高速列车传动系统故障诊断与健康状态预测[D]. 长春: 吉林大学交通学院, 2019.
Qiao Ning-guo. Fault diagnosis and health prediction of high-speed train transmission system based on multi-sensor fusion[D]. Changchun: College of Transportation, Jilin University, 2019.
29 Blázquez-García A, Conde A, Mori U, et al. A review on outlier/anomaly detection in time series data[J]. ACM Computing Surveys, 2021, 54(3): 1-33.
30 Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6): 381-395.
31 雷达, 钟诗胜. 基于奇异值分解和经验模态分解的航空发动机健康信号降噪[J]. 吉林大学学报:工学版, 2013, 43(3): 764-770.
Lei Da, Zhong Shi-sheng. Aircraft engine health signal denoising based on singular value decomposition and empirical decomposition methods[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(3): 764-770.
32 孙延奎. 小波分析及其应用[M]. 北京: 清华大学, 2005.
33 周小龙, 徐鑫莉, 王尧, 等. 基于变分模态分解和最大重叠离散小波包变换的齿轮信号去噪方法[J]. 振动与冲击, 2021, 40(12): 265-274, 289.
Zhou Xiao-long, Xu Xin-li, Wang Yao, et al. A gear signal denoising method based on variational mode decomposition and maximal overlap discrete wavelet packet transform[J]. Journal of Vibration and Shock, 2021, 40(12): 265-274, 289.
34 Yang F, Cui Y C, Wu F, et al. Fault monitoring of chemical process based on sliding window wavelet denoisingGLPP[J]. Processes, 2021, 9(1): No.86.
35 Anowar F, Sadaoui S, Selim B. Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)[J]. Computer Science Review, 2021, 40: No.100378.
36 Wang T, Han Q, Chu F, et al. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review[J]. Mechanical Systems and Signal Processing, 2019, 126: 662-685.
37 赵帅, 黄亦翔, 王浩任, 等. 基于拉普拉斯特征马氏距离的滚珠丝杠健康评估[J]. 机械工程学报, 2017, 53(15): 125-130.
Zhao Shuai, Huang Yi-xiang, Wang Hao-ren, et al. Laplacian eigenmaps and mahalanobis distance based health assessment methodology for ball screw[J]. Journal of Mechanical Engineering, 2017, 53(15): 125-130.
38 Shao R, Hu W, Wang Y, et al. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform[J]. Measurement, 2014, 54: 118-132.
39 Yang Y, Li X, Liu X, et al. Wavelet kernel entropy component analysis with application to industrial process monitoring[J]. Neurocomputing, 2015, 147: 395-402.
40 Yan J, Zhao Z, Liu H, et al. Fault detection and identification for quadrotor based on airframe vibration signals: a data-driven method[C]∥Proceedings of the 34th Chinese Control Conference, Hangzhou, China, 2015: 6356-6361.
41 Rafiee J, Arvani F, Harifi A, et al. Intelligent condition monitoring of a gearbox using artificial neural network[J]. Mechanical Systems and Signal Processing, 2007, 21(4): 1746-1754.
42 张新鹏. 压缩感知及其在旋转机械健康监测中的应用[D]. 长沙: 国防科学技术大学智能科学学院, 2015.
Zhang Xin-peng. Application research on compressed sensing in health monitoring of rotating machinery[D]. Changsha: College of Intelligence Science and Technology, National University of Defense Technology, 2015.
43 杜小磊, 陈志刚, 张楠, 等. 压缩感知和改进深层小波网络在轴承故障诊断中的应用[J]. 机械强度, 2020, 42(4): 777-785.
Du Xiao-lei, Chen Zhi-gang, Zhang Nan, et al. Application of compressive sensing and improved deep wavelet neural network in bearing fault diagnosis[J]. Journal of Mechanical Strength, 2020, 42(4): 777-785.
44 Jiang W, Mu L, Zhang X. A new method of power system fault recording based on compressed sensing[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2017, 12(4): 546-552.
45 岳研, 刘畅, 刘韬. 基于深度融合神经网络的轴承健康指标构建[J]. 电子测量与仪器学报, 2021, 35(7): 44-52.
Yue Yan, Liu Chang, Liu Tao. Deep fusion neural network for health indicator construction of bearings[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(7): 44-52.
46 Ellefsen A L, Æsøy V, Ushakov S, et al. A comprehensive survey of prognostics and health management based on deep learning for autonomous ships[J]. IEEE Transactions on Reliability, 2019, 68(2): 720-740.
47 Guo J, Li Z, Li M. A review on prognostics methods for engineering systems[J]. IEEE Transactions on Reliability, 2020, 69(3): 1110-1129.
48 任浩, 屈剑锋, 柴毅, 等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8): 1345-1358.
Ren Hao, Qu Jian-feng, Chai Yi, et al. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8): 1345-1358.
49 胡寿松. 自动控制原理[M].6版. 北京: 科学出版社, 2015.
50 Cai K-Y, Wen C Y, Zhang M L. Fuzzy reliability modeling of gracefully degradable computing systems[J]. Reliability Engineering & System Safety, 1991, 33(1): 141-157.
51 Cai K-Y. Introduction to Fuzzy Reliability[M]. London: Kluwer Academic Publishers, 1996.
52 Zhao Z, Quan Q, Cai K-Y. A profust reliability based approach to prognostics and health management[J]. IEEE Transactions on Reliability, 2014, 63(1): 26-41.
53 Zhao Z, Quan Q, Cai K-Y. A modified profust-performance-reliability algorithm and its application to dynamic systems[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32(1): 643-660.
54 Patton R J, Chen J. Observer-based fault detection and isolation: robustness and applications[J]. Control Engineering Practice, 1997, 5(5): 671-682.
55 Heredia G, Ollero A, Béjar M, et al. Sensor and actuator fault detection in small autonomous helicopters[J]. Mechatronics, 2008, 18(2): 90-99.
56 Julier S J, Uhlrnann J K, Durrant-Whyte H F. A new approach for filtering nonlinear systems[C]∥Proceedings of 1995 American Control Conference, Seattle, Washington, USA, 1995: No.529783.
57 刘亚姣, 刘振泽, 宋晨辉. 基于改进粒子滤波的锂离子电池RUL预测[J]. 吉林大学学报: 信息科学版, 2018, 36(2): 173-177.
Liu Ya-jiao, Liu Zhen-ze, Song Chen-hui. Improved particle filter algorithm for RUL prediction of lithium-ion batteries[J]. Journal of Jilin University (Information Science Edition), 2018, 36(2): 173-177.
58 刘斌, 杨斌先, 赵峙尧, 等. 一种四旋翼飞行控制能力实时评估方法[C]∥第33届中国控制会议论文集, 南京, 2014: 3112-3117.
59 Lu P, Van Kampen E J, Yu B. Actuator fault detection and diagnosis for quadrotors[C]∥IMAV 2014: International Micro Air Vehicle Conference and Competition 2014, The Netherlands, 2014: 3112-3117.
60 邓涛, 罗卫兴, 李志飞, 等. 双卡尔曼滤波法估计电动汽车电池健康状态[J]. 电池, 2018, 48(2): 95-99.
Deng Tao, Luo Wei-xing, Li Zhi-fei, et al Estimation state of health of electric vehicle battery by dual Kalman filter[J]. Battery Bimonthly, 2018, 48(2): 95-99.
61 葛江华, 刘奇, 王亚萍, 等. 支持张量机与 KNN-AMDM 决策融合的齿轮箱故障诊断方法[J]. 振动工程学报, 2018, 31(6): 1093-1101.
Ge Jiang-hua, Liu Qi, Wang Ya-ping, et al. Fault diagnosis method of gearbox supporting tension machine and KNN-AMDM decision fusion[J]. Journal of Vibration Engineering, 2018, 31(6): 1093-1101.
62 Yang J, Sun Z, Chen Y. Fault detection using the clustering-kNN rule for gas sensor arrays[J]. Sensors, 2016, 16(12): No.2069.
63 He Q P, Wang J. Principal component based k-nearest-neighbor rule for semiconductor process fault detection[J/OL]. [2022-03-20].
64 Ali M Z, Shabbir M N S K, Liang X, et al. Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals[J]. IEEE Transactions on Industry Applications, 2019, 55(3): 2378-2391.
65 Sadhu V, Zonouz S, Pompili D. On-board deep-learning-based unmanned aerial vehicle fault cause detection and identification[C]∥2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020:No.00336.
66 温江涛, 闫常弘, 孙洁娣, 等. 基于压缩采集与深度学习的轴承故障诊断方法[J]. 仪器仪表学报, 2018, 39(1): 171-179.
Wen Jiang-tao, Yan Chang-hong, Sun Jie-di, et al. Bearing fault diagnosis method based on compressed acquisition and deep learning[J]. Chinese Journal of Scientific Instrument, 2018, 39(1): 171-179.
67 田书, 康智慧. 基于改进变分模态分解和 SVM 的断路器机械故障振动分析[J]. 振动与冲击, 2019, 38(23): 90-95.
Tian Shu, Kang Zhi-hui. Circuit breaker mechanical fault vibration analysis based on improved variational mode decomposition and SVM[J]. Journal of Vibration and Shock, 2019, 38(23): 90-95.
68 时培明, 梁凯, 赵娜, 等. 基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断[J]. 中国机械工程, 2017, 28(9): 1056-1061, 1068.
Shi Pei-ming, Liang Kai, Zhao Na, et al. Intelligent fault diagnosis for gears based on deep learning feature extraction and particle swarm optimization svm state identification[J]. China Mechanical Engineering, 2017, 28(9): 1056-1061, 1068.
69 Emran B J, Najjaran H. A review of quadrotor: an underactuated mechanical system[J]. Annual Reviews in Control, 2018, 46: 165-180.
70 杜光勋, 全权. 输入受限系统的可控度及其在飞行控制中的应用[J]. 系统科学与数学, 2014, 34(12): 1578-1594.
Du Guang-xun, Quan Quan. Degree of controllability and its application in aircraft flight control[J]. Journal of Systems Science and Mathematical Sciences, 2014, 34(12): 1578-1594.
71 Du G X, Quan Q, Yang B, et al. Controllability analysis for multirotor helicopter rotor degradation and failure[J]. Journal of Guidance, Control, and Dynamics, 2015, 38(5): 978-985.
72 Kang O, Park Y, Park Y S, et al. New measure representing degree of controllability for disturbance rejection[J]. Journal of Guidance, Control, and Dynamics, 2009, 32(5): 1658-1661.
73 Quan Q, Cui G, Du G X. Controllable probability and optimization of multicopters[J]. Aerospace Science and Technology, 2021, 119: No.107162.
74 Du G X, Quan Q. Optimization of multicopter propulsion system based on degree of controllability[J]. Journal of Aircraft, 2019, 56(5): 2062-2069.
75 许树柏. 实用决策方法——层次分析法原理[M]. 天津: 天津大学出版社, 1988.
76 张炳江. 层次分析法及其应用案例[M]. 北京: 电子工业出版社, 2014.
77 史定华, 王松瑞. 故障树分析技术方法和理论[M]. 北京: 北京师范大学出版社, 1993.
78 Spreafico C, Russo D, Rizzi C. A state-of-the-art review of FMEA/FMECA including patents[J]. Computer Science Review, 2017, 25: 19-28.
79 王立平, 朱斌, 吴军, 等. 基于贝叶斯网络的盘式刀库故障分析[J]. 吉林大学学报 :工学版, 2022, 52(2): 280-287.
Wang Li-ping, Zhu Bin, Wu Jun, et al. Fault analysis of circular tool magazine based on Bayesian network[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(2): 280-287.
80 徐进永, 罗士军, 张子达. 基于模糊故障树分析法的装载机液压系统故障诊断系统[J]. 吉林大学学报 :工学版, 2007, 37(3): 569-574.
Xu Jin-yong, Luo Shi-jun, Zhang Zi-da. Fault diagnosis system of wheel loader hydraulic system based on fuzzy fault tree analysis[J]. Journal of Jilin University (Engineering and Technology Edition), 2007, 37(3): 569-574.
81 Cai K-Y. Variable-structure coherent systems[J]. International Journal of General Systems, 2005, 34(6): 639-672.
82 Gao Z, Liu X. An overview on fault diagnosis, prognosis and resilient control for wind turbine systems[J]. Processes, 2021, 9(2): 1-19.
83 Zhang Z, Si X, Hu C, et al. Degradation data analysis and remaining useful life estimation: A review on wiener-process-based methods[J]. European Journal of Operational Research, 2018, 271(3): 775-796.
84 Si X S, Wang W, Hu C H, et al. Remaining useful life estimation based on a nonlinear diffusion degradation process[J]. IEEE Transactions on Reliability, 2012, 61(1): 50-67.
85 Zhao Z, Quan Q, Cai K-Y. A health performance prediction method of large-scale stochastic linear hybrid systems with small failure probability[J]. Reliability Engineering & System Safety, 2017, 165: 74-88.
86 Kordestani M, Saif M, Orchard M E, et al. Failure prognosis and applications—a survey of recent literature[J]. IEEE Transactions on Reliability, 2021, 70(2): 728­748.
87 Pecht M, Gu J. Physics-of-failure-based prognostics for electronic products[J]. Transactions of the Institute of Measurement and Control, 2009, 31(3/4): 309-322.
88 Byington C, Watson M, Edwards D, et al. A model-based approach to prognostics and health management for flight control actuators[C]∥2004 IEEE Aerospace Conference Proceedings, SkyBig, MT, USA, 2004: 3551-3562.
89 Hosseini Toudeshky H, Jahanmardi M, Goodarzi M S. Progressive debonding analysis of composite blade root joint of wind turbines under fatigue loading[J]. Composite Structures, 2015, 120: 417-427.
90 Cárdenas D, Elizalde H, Marzocca P, et al. A coupled aeroelastic damage progression model for wind turbine blades[J]. Composite Structures, 2012, 94(10): 3072-3081.
91 Wang Q, Wang C, Sun Q. A model-based time-to-failure prediction scheme for nonlinear systems via deterministic learning[J]. Journal of the Franklin Institute, 2020, 357(6): 3771-3791.
92 全权, 张贺鹏. 一种基于有限维分布的碰撞概率实时评估方法[P]. 中国: ZL201811465777.X, 2019-04-16.
93 Kulkarni V G. Modeling, Analysis, Design, and Control of Stochastic Systems[M]. New York: Springer, 1999.
94 Frey K M, Steiner T J, How J P. Collision probabilities for continuous-time systems without sampling[J/OL].[2022-03-21].
95 Zhang J X, Du D B, Si X S, et al. Prognostics based on stochastic degradation process: the last exit time perspective[J]. IEEE Transactions on Reliability, 2021, 70(3): 1158-1176.
96 Box G E P, Jenkins G M, Reinsel G C, et al. Time series Analysis: Forecasting and Control[M]. San Francisco: Holden-Day, 2015.
97 周圆. 基于健康状态的变压器运维策略及经济寿命研究[D]. 北京: 华北电力大学电子与电子工程学院, 2019.
Zhou Yuan. Research on transformer operation and maintenance strategy and economic life based on health status[D]. Beijing: School of Electrical and Electronic Engineering, North China Electric Power University, 2019.
98 霍家志. 基于时间序列分析的电池寿命预测算法研究[D]. 成都: 电子科技大学计算机科学与工程学院, 2020.
Huo Jia-zhi. Research on battery life prediction algorithm based on time series analysis[D]. Chengdu: School of Computer Science and Engineering, University of Electronic Science and Technology of China, 2020.
99 Koriyama T. An introduction of gaussian processes and deep gaussian processes and their applications to speech processing[J]. Acoustical Science and Technology, 2020, 41(2): 457-464.
100 何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129, 1137.
He Zhi-kun, Liu Guang-bin, Zhao Xi-jing, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129, 1137.
101 Herp J, Ramezani M H, Bach-Andersen M, et al. Bayesian state prediction of wind turbine bearing failure[J]. Renewable Energy, 2018, 116: 164-172.
102 Kordestani M, Samadi M F, Saif M, et al. A new fault prognosis of MFS system using integrated extended kalman filter and bayesian method[J].IEEE Transactions on Industrial Informatics,2018, PP(99):No.1.
103 庞景月, 马云彤, 刘大同, 等. 锂离子电池剩余寿命间接预测方法[J]. 中国科技论文, 2014, 9(1): 28-36.
Pang Jing-yue, Ma Yun-tong, Liu Da-tong, et al. Indirect remaining useful life prognostics for lithium-ion battery[J]. China Science Paper, 2014, 9(1): 28-36.
104 曾友渝, 谢强. 基于改进RNN和VAR的船舶设备故障预测方法[J]. 计算机科学, 2021, 48(6): 184-189.
Zeng You-yu, Xie Qiang. Fault prediction method based on improved RNN and VAR for ship equipment[J]. Computer Science, 2021, 48(6): 184-189.
105 陈远航. 滚动轴承剩余寿命预测算法研究及监测软件开发[D]. 哈尔滨: 哈尔滨工业大学机电工程学院, 2020.
Chen Yuan-hang. Study on algorithm for rolling bearing remaining useful life prediction and development of monitor software[D]. Harbin: School of Mechatronics Engineering, Harbin Institute of Technology, 2020.
106 Xu J P, Xu L. Integrated System Health Management[M]. Cambridge, Massachusetts: Academic Press, 2017.
107 Zhang Y, Jiang J. Bibliographical review on reconfigurable fault-tolerant control systems[J]. Annual Reviews in Control, 2008, 32(2): 229-252.
108 Ke C X, Cai K Y, Quan Q. Uniform fault-tolerant control of a quadcopter with rotor failure[J]. IEEE/ASME Transactions on Mechatronics, 2022, PP(99):1-11.
109 Cai K-Y, Trivedi K S, Yin B. S-ADA: software as an autonomous, dependable and affordable system[C]∥2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks-Supplemental, Taipei, China, 2021: 17-18.
110 OpenHA[DB/OL]. [2022-10-20].
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