数据驱动的武器系统电子元部件级故障诊断研究综述

doi:10.12305/j.issn.1001-506X.2021.02.33

摘要/Abstract

摘要：

电子元部件级状态监测与故障诊断技术是武器装备维修保障的关键，是武器系统作战效能保持和快速恢复的可靠保障。随着数据获取、存储及挖掘技术的快速发展，基于数据驱动的智能诊断方法逐渐成为状态监测与故障诊断领域的重要研究方向。武器系统电子元部件集成形式多样、工作环境复杂、参数指标多、故障模式动态耦合，给故障诊断工程实现带来了严峻的挑战，特别是数据质量问题、故障诊断方法与应用问题以及复杂运行环境下未知故障模式识别问题。针对当前武器系统电子元部件级故障诊断对象、数据驱动的诊断方法以及面临的主要问题进行简要综述，在总结国内外已有研究成果的基础上，指出了在未来实现武器系统电子元部件级的状态监测与在线故障诊断技术的发展趋势。

关键词: 数据驱动, 武器系统, 电子元部件, 状态监测, 故障诊断

Abstract:

Electronic components and units level state monitoring and fault diagnosis technology is the key of weapon equipment maintenance support, and which is the reliable guarantee of weapon system combat effectiveness maintenance and rapid recovery. With the rapid development of data acquisition, storage and mining technology, the intelligent diagnosis method based on data-driven has gradually become an important research direction in the field of state monitoring and fault diagnosis. The integration of electronic components and units of weapon system is diverse, the working environment is complex, the parameters are multiple, and the fault modes are coupled dynamically, which brings severe challenges to the realization of fault diagnosis engineering, especially the problems of data quality, fault diagnosis method and application, and unknown fault pattern recognition in complex operating environment. The fault diagnosis objects, diagnosis methods of data-driven and main problems of electronic components and units level of weapon system are briefly reviewed. Based on the research results at home and abroad, the development trend of state monitoring and on-line fault diagnosis technology of electronic components and units level of weapon system in the future are pointed out.

Key words: data-driven, weapon system, electronic component and unit, state monitoring, fault diagnosis

中图分类号:

TJ07

孟晨, 杨华晖, 王成, 马征. 数据驱动的武器系统电子元部件级故障诊断研究综述[J]. 系统工程与电子技术, 2021, 43(2): 574-583.

Chen MENG, Huahui YANG, Cheng WANG, Zheng MA. Review on data-driven fault diagnosis for electronic components and units level of weapon system[J]. Systems Engineering and Electronics, 2021, 43(2): 574-583.

图/表 8

图1

图2

图3

图4

图5

图6

表1

图7

参考文献 76

1	侯晓东, 王永攀, 杨江平, 等. 基于状态的武器电子装备故障预测研究综述[J]. 系统工程与电子技术, 2018, 40 (2): 360- 367.
	HOU X D , WANG Y P , YANG J P , et al. Research summary of weapon electronic equipment fault prediction based on state[J]. Systems Engineering and electronics, 2018, 40 (2): 360- 367.
2	文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42 (9): 1285- 1299.
	WEN C L , LYU F Y , BAO Z J , et al. A review of data driven-based incipient fault diagnosis[J]. Acta Automatica Sinica, 2016, 42 (9): 1285- 1299.
3	文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42 (1): 234- 248.
	WEN C L , LYU F Y . Review on deep learning based fault diagnosis[J]. Journal of Electronic & Information Technology, 2020, 42 (1): 234- 248.
4	苗建国, 王剑宇, 张恒, 等. 无人机故障诊断技术研究进展概述[J]. 仪器仪表学报, 2020, 41 (9): 56- 59.
	MIAO J G , WANG J Y , ZHANG H , et al. Review of the development of fault diagnosis technology for unmanned aerial vehicle[J]. Chinese Journal of Scientific Instrument, 2020, 41 (9): 56- 59.
5	ZHANG W T , YANG D , WANG H C . Data-driven methods for predictive maintenance of industrial equipment: a survey[J]. IEEE Systems Journal, 2019, 13 (3): 2213- 2227. doi: 10.1109/JSYST.2019.2905565
6	XU Y , SUN Y M , WAN J F , et al. Industrial big data for fault diagnosis: taxonomy, review, and applications[J]. IEEE Access, 2017, 5, 17368- 17380. doi: 10.1109/ACCESS.2017.2731945
7	CHANG B L , YANG R F , GUO C X , et al. A new application of optimized random forest algorithms in intelligent fault location of rudders[J]. IEEE Access, 2019, 7, 94276- 94283. doi: 10.1109/ACCESS.2019.2926109
8	CAI J Y , HAN C H , MENG Y F . Analog circuit testability for fault diagnosis[J]. Tsinghua Science and Technology, 2007, 12 (S1): 270- 274. doi: 10.1016/S1007-0214(07)70123-2
9	ZHANG H , LIANG J , ZHANG Z Y . Active fault tolerant control of adaptive cruise control system considering vehicle-borne millimeter wave radar sensor failure[J]. IEEE Access, 2020, 8, 11228- 11240. doi: 10.1109/ACCESS.2020.2964947
10	TIDRIRI K , CHATTI N , VERRON S , et al. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges[J]. Annual Review in Control, 2016, 42, 63- 81. doi: 10.1016/j.arcontrol.2016.09.008
11	FREIRE N M A , ESTIMA J O , CARDOSO A J M . A voltage-based approach without extra hardware for open-circuit fault diagnosis in closed-loop PWM AC regenerative drives[J]. IEEE Trans.on Industrial Electronics, 2014, 61 (9): 4960- 4970. doi: 10.1109/TIE.2013.2279383
12	WANG L , PAN J , GAO Y F , et al. Incipient fault diagnosis of limit switch based on a ARMA model[J]. Measurement, 2019, 135, 473- 480. doi: 10.1016/j.measurement.2018.11.080
13	MAHAPATRO A , KHILAR P M . Fault diagnosis in wireless sensor networks: a survey[J]. Communications Surveys & Tutorials, 2013, 15 (4): 2000- 2026.
14	YIN S , DING S X , XIE X C , et al. A review on basic data-driven approaches for industrial process monitoring[J]. IEEE Trans.on Industrial Electronics, 2014, 61 (11): 6418- 6428. doi: 10.1109/TIE.2014.2301773
15	DAI X W , GAO Z W . From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis[J]. IEEE Trans.on Industrial Informatics, 2018, 9 (4): 2226- 2238.
16	MOSTAFA M Z , KHATER H A , RIZK M R , et al. GPS/DVL/MEMS-INS smartphone sensors integrated method to enhance USV navigation system based on adaptive DSFCF[J]. IET Radar, Sonar & Navigation, 2017, 13 (10): 1616- 1627.
17	BU J , SUN R , BAI H Y , et al. Integrated method for the UAV navigation sensor anomaly detection[J]. IET Radar Sonar & Navigation, 2017, 11 (5): 847- 853.
18	SHAN G L , XU G G , QIAO C L . A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control[J]. Defence Technology, 2020, 16 (1): 242- 250. doi: 10.1016/j.dt.2019.10.001
19	SHIROKOV L E . Integrated aircraft control for rendezvous of aircraft-launched missiles with radiating targets[J]. Journal of Computer and Systems Sciences International Volume, 2014, 53 (2): 291- 304. doi: 10.1134/S1064230714020142
20	MINOR C P , STEINHURST D A , JOHNSON K J , et al. A full-scale prototype multisensor system for damage control and situatio-nal awareness[J]. Fire Technology, 2010, 46 (2): 437- 469. doi: 10.1007/s10694-009-0103-y
21	GUO C J , LI F , TIAN Z , et al. Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network[J]. Neural Computing and Applications, 2019, 32, 16857- 16874.
22	XU H W , LIAN B W . Fault detection for multi-source integrated navigation system using fully convolutional neural network[J]. IET Radar Sonar & Navigation, 2018, 12 (7): 774- 782.
23	SUN R , CHENG Q , WANG G Y , et al. A novel online data-driven algorithm for detecting UAV navigation sensor faults[J]. Sensors, 2017, 17 (10): 2243. doi: 10.3390/s17102243
24	FRAVOLINI M L , CORE G D , PAPA U , et al. Data-driven schemes for robust fault detection of air data system sensors[J]. IEEE Trans.on Control Systems Technology, 2017, 27 (1): 234- 248.
25	SAMARA P A , FOUSKITAKIS G N , SAKELLARIOU J S , et al. A statistical method for the detection of sensor abrupt faults in aircraft control systems[J]. IEEE Trans.on Control Systems Technology, 2008, 16 (4): 789- 798. doi: 10.1109/TCST.2007.903109
26	GOU B , XU Y , XIA Y , et al. An intelligent time-adaptive data-driven method for sensor fault diagnosis in induction motor drive system[J]. IEEE Trans.on Industrial Electronics, 2018, 66 (12): 9817- 9827.
27	DONG L , JATSKEVICH J , HUANG Y , et al. Fault diagnosis and signal reconstruction of hall sensors in brushless permanent magnet motor drives[J]. IEEE Trans.on Energy Conversion, 2015, 31 (1): 118- 131.
28	BINU D , KARIYAPPA B S . A survey on fault diagnosis of analog circuits: taxonomy and state of the art[J]. AEU-International Journal of Electronics and Communications, 2017, 73, 68- 83. doi: 10.1016/j.aeue.2017.01.002
29	ZHANG T W , LI T J . A novel approach of analog circuit fault diagnosis utilizing RFT noise estimation[J]. Analog Integrated Circuits and Signal Processing, 2019, 98 (3): 517- 526. doi: 10.1007/s10470-018-1351-x
30	ZHAO G Q , LIU X Y , ZHANG B , et al. A novel approach for analog circuit fault diagnosis based on deep belief network[J]. Measurement, 2018, 121, 170- 178. doi: 10.1016/j.measurement.2018.02.044
31	ABDERRAZAK A , NACERDINE B , ABDESSLAM B , et al. An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits[J]. Integration, 2019, 64, 50- 59.
32	YANG H H , MENG C , WANG C . Data-driven feature extraction for analog circuit fault diagnosis using 1-D convolutional neural network[J]. IEEE Access, 2020, 8, 18305- 18315. doi: 10.1109/ACCESS.2020.2968744
33	GAN X S , GAO W M , DAI Z , et al. Research on WNN soft fault diagnosis for analog circuit based on adaptive UKF algorithm[J]. Applied Soft Computing, 2016, 50, 252- 259.
34	YUAN Z J , HE Y G , YUAN L F , et al. An efficient feature extraction approach based on manifold learning for analogue circuits fault diagnosis[J]. Analog Integrated Circuits and Signal Processing, 2020, 102 (1): 237- 252. doi: 10.1007/s10470-018-1377-0
35	XIA J H , GUO Y B , DAI B J , et al. Sensor fault diagnosis and system reconfiguration approach for an electric traction PWM rectifier based on sliding mode observer[J]. IEEE Trans.on Industry Applications, 2017, 53 (5): 4768- 4778. doi: 10.1109/TIA.2017.2715816
36	WU F , ZHAO J . A real-time multiple open-circuit fault diagnosis method in voltage-source-inverter fed vector controlled drives[J]. IEEE Trans.on Power Electronics, 2016, 31 (2): 1425- 1437. doi: 10.1109/TPEL.2015.2422131
37	WU F , ZHAO J , LIU Y , et al. Primary source inductive energy analysis based real-time multiple open-circuit fault diagnosis in two-level three-phase PWM boost rectifier[J]. IEEE Trans.on Power Electronics, 2017, 33 (4): 3411- 3424.
38	CHENG S , LI W , DING R J , et al. Fault diagnosis and fault-tolerant control scheme for open-circuit faults in three-stepped bridge converters[J]. IEEE Trans.on Power Electronics, 2017, 32 (3): 2203- 2214. doi: 10.1109/TPEL.2016.2558491
39	YAN H , XU Y X , CAI F Y , et al. PWM-VSI fault diagnosis for a PMSM drive based on the fuzzy logic approach[J]. IEEE Trans.on Power Electronics, 2019, 34 (1): 759- 768. doi: 10.1109/TPEL.2018.2814615
40	YANG C , GUI W H , CHEN Z W , et al. Voltage difference residual-based open-circuit fault diagnosis approach for three-level converters in electric traction systems[J]. IEEE Trans.on Power Electronics, 2020, 35 (3): 3012- 3028. doi: 10.1109/TPEL.2019.2924487
41	HU M , WANG H , HU G , et al. Soft fault diagnosis for analog circuits based on slope fault feature and BP neural networks[J]. Tsinghua Science and Technology, 2007, 12 (S1): 26- 31. doi: 10.1016/S1007-0214(07)70079-2
42	RUAN S , ZHOU Y K , YU F L , et al. Dynamic multiple-fault diagnosis with imperfect tests[J]. IEEE Trans.on Systems, Man and Cybernetics, Part A: Systems and Humans, 2009, 39 (6): 1224- 1236. doi: 10.1109/TSMCA.2009.2025572
43	SINGH S , KODALI A , CHOI K , et al. Dynamic multiple fault diagnosis: mathematical formulations and solution techniques[J]. IEEE Trans.on Systems Man and Cybernetics-Part a Systems and Humans, 2009, 39 (1): 160- 176. doi: 10.1109/TSMCA.2008.2007986
44	WU F , ZHAO J . Current similarity analysis based open-circuit fault diagnosis for two-level three-phase PWM rectifier[J]. IEEE Trans.on Power Electronics, 2017, 32 (5): 3935- 3945. doi: 10.1109/TPEL.2016.2587339
45	VONG C M , WONG P K , IP W F . A new framework of simultaneous-fault diagnosis using pairwise probabilistic multi-label classification for time-dependent patterns[J]. IEEE Trans.on Industrial Electronics, 2013, 60 (8): 3372- 3385. doi: 10.1109/TIE.2012.2202358
46	CHOQUEUSE V , BENBOUZID M E H , AMIRAT Y , et al. Diagnosis of three-phase electrical machines using multidimensional demodulation techniques[J]. IEEE Trans.on Industrial Electronics, 2012, 59 (4): 2014- 2023. doi: 10.1109/TIE.2011.2160138
47	YANG T , PEN H B , WANG Z X , et al. Feature knowledge based fault detection of induction motors through the analysis of stator current data[J]. IEEE Trans.on Instrumentation & Measurement, 2016, 65 (3): 549- 558.
48	LEE J M , YOO C K , LEE I B . Statistical process monitoring with independent component analysis[J]. Journal of Process Control, 2004, 14 (5): 467- 485. doi: 10.1016/j.jprocont.2003.09.004
49	MURADORE R . A PLS-based statistical approach for fault detection and isolation of robotic manipulators[J]. IEEE Trans.on Industrial Electronics, 2012, 59 (8): 3167- 3175. doi: 10.1109/TIE.2011.2167110
50	YIN S , ZHU X P , KAYNAK O . Improved PLS focused on key-performance-indicator-related fault diagnosis[J]. IEEE Trans.on Industrial Electronics, 2015, 62 (3): 1651- 1658. doi: 10.1109/TIE.2014.2345331
51	ZHONG K , HAN M , QIU T , et al. Fault diagnosis of complex processes using sparse kernel local fisher discriminant analy-sis[J]. IEEE Trans.on Neural Networks and Learning Systems, 2020, 31 (5): 1581- 1591. doi: 10.1109/TNNLS.2019.2920903
52	FENG J , WANG J , ZHANG H G , et al. Fault diagnosis method of joint fisher discriminant analysis based on the local and global manifold learning and its kernel version[J]. IEEE Trans.on Automation Science & Engineering, 2016, 13 (1): 122- 133.
53	DELPHA C , DIALLO D , SAMROUT H A , et al. Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing[J]. Engineering Applications of Artificial Intelligence, 2018, 73, 68- 79. doi: 10.1016/j.engappai.2018.04.007
54	PILARIO K E S , CAO Y . Canonical variate dissimilarity analysis for process incipient fault detection[J]. IEEE Trans.on Industrial Informatics, 2018, 14 (12): 5308- 5315. doi: 10.1109/TII.2018.2810822
55	SHI J Y , HE Q J , WANG Z L . GMM clustering-based decision trees considering fault rate and cluster validity for analog circuit fault diagnosis[J]. IEEE Access, 2019, 7, 140637- 140650. doi: 10.1109/ACCESS.2019.2943380
56	YIN S , HUANG Z H . Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements[J]. IEEE/ASME Trans.on Mechatronics, 2015, 20 (5): 2613- 2620. doi: 10.1109/TMECH.2014.2358674
57	YIN S , ZHU X P , JING C . Fault detection based on a robust one class support vector machine[J]. Neurocomputing, 2014, 145, 263- 268. doi: 10.1016/j.neucom.2014.05.035
58	HE Y , DU C Y , LI C B , et al. Sensor fault diagnosis of superconducting fault current limiter with saturated iron core based on SVM[J]. IEEE Trans.on Applied Superconductivity, 2014, 24 (5): 5602805.
59	ZIDI S , MOULAHI T , ALAYA B . Fault detection in wireless sensor networks through SVM classifier[J]. IEEE Sensors Journal, 2018, 18 (1): 340- 347. doi: 10.1109/JSEN.2017.2771226
60	SUO M L , ZHU B L , AN R M , et al. Data-driven fault diagnosis of satellite power system using fuzzy Bayes risk and SVM[J]. Aerospace Science & Technology, 2019, 84, 1092- 1105.
61	CHEN P , YUAN L F , HE Y G , et al. An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis[J]. Neurocompu-ting, 2016, 211, 202- 211. doi: 10.1016/j.neucom.2015.12.131
62	KURAKU N V P , HE Y , SHI T , et al. Fuzzy logic based open-circuit fault diagnosis in IGBT for CMLI fed PMSM drive[J]. Microelectronics Reliability, 2019, 100-101, 113415. doi: 10.1016/j.microrel.2019.113415
63	KUMAR A , SINGH A P . Fuzzy classifier for fault diagnosis in analog electronic circuits[J]. ISA Transactions, 2013, 52 (6): 816- 824. doi: 10.1016/j.isatra.2013.06.006
64	YU W X , SUI Y , WANG J . The faults diagnostic analysis for analog circuit based on FA-TM-ELM[J]. Journal of Electronic Testing, 2016, 32 (4): 459- 465. doi: 10.1007/s10836-016-5597-x
65	GAN X S , QU H , MENG X W , et al. Research on ELM soft fault diagnosis of analog circuit based on KSLPP feature extraction[J]. IEEE Access, 2019, 7, 92517- 92527. doi: 10.1109/ACCESS.2019.2923242
66	MAIDEN Y , JERVIS B W , DUTTON N , et al. Diagnosis of multifaults in analogue circuits using multilayer perceptrons[J]. IEE Proceedings-Circuits Devices and Systems, 1997, 144 (3): 149- 154. doi: 10.1049/ip-cds:19971146
67	YU S , JERVIS B W , ECKERSALL K R , et al. Diagnosis of CMOS op-amps with gate oxide short faults using multilayer perceptrons[J]. IEEE Trans.on Computer-Aided Design of Integrated Circuits and Systems, 1997, 16 (8): 930- 935. doi: 10.1109/43.644623
68	SONG P , HE Y Z , CUI W J . Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits[J]. Analog Integrated Circuits & Signal Processing, 2016, 87 (3): 427- 436.
69	LUO H , WANG Y R , CUI J . A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor[J]. Expert Systems with Applications, 2011, 38 (8): 10554- 10561. doi: 10.1016/j.eswa.2011.02.087
70	JEONG K , CHOI S B , CHOI H . Sensor fault detection and isolation using a support vector machine for vehicle suspension systems[J]. IEEE Trans.on Vehicular Technology, 2020, 69 (4): 3852- 3863. doi: 10.1109/TVT.2020.2977353
71	JAN S U , LEE Y D , SHIN J , et al. Sensor fault classification based on support vector machine and statistical time-domain features[J]. IEEE Access, 2017, 5, 8682- 8690. doi: 10.1109/ACCESS.2017.2705644
72	JIANG Q C , YAN S F , YAN X F , et al. Data-driven two-dimensional deep correlated representation learning for nonlinear batch process monitoring[J]. IEEE Trans.on Industrial Informatics, 2020, 16 (4): 2839- 2848. doi: 10.1109/TII.2019.2952931
73	ZHANG C L , HE Y G , YUAN L F , et al. Analog circuit incipient fault diagnosis method using DBN based features extraction[J]. IEEE Access, 2018, 6, 23053- 23064. doi: 10.1109/ACCESS.2018.2823765
74	SHI T C , HE Y G , WANG T , et al. Open switch fault diagnosis method for PWM voltage source rectifier based on deep learning approach[J]. IEEE Access, 2019, 7, 66595- 66608. doi: 10.1109/ACCESS.2019.2917311
75	WANG Z S , LIU L , ZHANG H G . Neural network-based model-free adaptive fault-tolerant control for discrete-time nonlinear systems with sensor fault[J]. IEEE Trans.on Systems, Man, and Cybernetics, 2017, 47 (8): 2351- 2362. doi: 10.1109/TSMC.2017.2672664
76	陈彧赟, 侯博文, 何章鸣, 等. 数据驱动的复杂系统非预期故障诊断通用过程模型[J]. 国防科技大学学报, 2017, 39 (6): 126- 133.
	CHEN Y Y , HOU B W , HE Z M , et al. General process mo-del for unanticipated fault diagnosis of complex system based on data driven[J]. Journal of National University of Defense Technology, 2017, 39 (6): 126- 133.

多元统计方法	高斯分布	非高斯分布	静态过程	动态过程	线性问题	非线性问题
主成分分析	√	—	√	—	√	—
独立分量分析	√	√	√	—	√	—
偏最小二乘	√	—	√	—	√	—
线性判别分析	√	—	√	—	√	—
Fisher判别分析	√	—	√	—	√	—
标准变量分析	√	—	√	√	√	—
聚类分析	√	√	√	√	√	√

[1]	高升, 马广富, 郭延宁. 基于自适应未知输入观测器的多故障快速重构[J]. 系统工程与电子技术, 2022, 44(7): 2364-2373.
[2]	侯召国, 王华伟, 周良, 付强. 基于改进深度残差网络的旋转机械故障诊断[J]. 系统工程与电子技术, 2022, 44(6): 2051-2059.
[3]	黄嘉, 常思江. 基于数据驱动的攻击时间和攻击角度控制导引律[J]. 系统工程与电子技术, 2022, 44(10): 3213-3220.
[4]	刘星, 王文双, 赵建印, 朱敏. 自适应在线增量ELM的故障诊断模型研究[J]. 系统工程与电子技术, 2021, 43(9): 2678-2687.
[5]	李睿峰, 许爱强, 孙伟超, 王树友. 基于元学习的航空电子设备特征选择算法推荐方法[J]. 系统工程与电子技术, 2021, 43(7): 2011-2020.
[6]	陈洪转, 赵爱佳, 李腾蛟, 蔡匆聪, 程硕, 徐春丽. 基于故障树的复杂装备模糊贝叶斯网络推理故障诊断[J]. 系统工程与电子技术, 2021, 43(5): 1248-1261.
[7]	王力, 刘子奇. WPA-IGA-BP神经网络的模拟电路故障诊断[J]. 系统工程与电子技术, 2021, 43(4): 1133-1143.
[8]	戴金玲, 许爱强. 基于动态软聚类的航空电子部件LMKELM诊断模型[J]. 系统工程与电子技术, 2021, 43(3): 637-646.
[9]	邢志伟, 刘洪恩, 李彪, 罗谦, 文涛, 陈肇欣. 基于时空关联网络的机场机位运行过程建模[J]. 系统工程与电子技术, 2021, 43(3): 722-730.
[10]	阳榴, 朱卫纲, 吕守业, 马爽. 面向非协作多功能雷达的波形单元提取方法[J]. 系统工程与电子技术, 2021, 43(10): 2843-2850.
[11]	马云飞, 贾希胜, 白华军, 郭驰名, 王双川. 基于一维CNN参数优化的压缩振动信号故障诊断[J]. 系统工程与电子技术, 2020, 42(9): 1911-1919.
[12]	王鹏, 杨妹, 祝建成, 鞠儒生, 李革. 面向数字孪生的动态数据驱动建模与仿真方法[J]. 系统工程与电子技术, 2020, 42(12): 2779-2786.
[13]	李波, 张琳, 汪文峰, 王冠男. 基于Park-HHT的故障特征提取方法[J]. 系统工程与电子技术, 2020, 42(12): 2944-2952.
[14]	姚天乐, 陶凤和, 胡起伟, 齐子元, 温亮. 基于多属性效用的主战坦克武器系统作战能力评估[J]. 系统工程与电子技术, 2019, 41(2): 358-364.
[15]	豆亚杰, 徐向前, 周哲轩, 夏博远, 杨克巍. 系统组合选择方法及典型军事应用[J]. 系统工程与电子技术, 2019, 41(12): 2754-2762.