Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1833-1841.DOI: 10.11772/j.issn.1001-9081.2022060808
• Data science and technology • Previous Articles Next Articles
Chaoshuai QI1, Wensi HE2, Yi JIAO3, Yinghong MA1(), Wei CAI4, Suping REN4
Received:
2022-06-06
Revised:
2022-11-09
Accepted:
2022-11-09
Online:
2023-06-08
Published:
2023-06-10
Contact:
Yinghong MA
About author:
QI Chaoshuai, born in 1998, M. S. candidate. His research interests include machine learning, data mining.Supported by:
祁超帅1, 何文思2, 焦毅3, 马英红1(), 蔡伟4, 任素萍4
通讯作者:
马英红
作者简介:
祁超帅(1998—),男,河南开封人,硕士研究生,主要研究方向:机器学习、数据挖掘基金资助:
CLC Number:
Chaoshuai QI, Wensi HE, Yi JIAO, Yinghong MA, Wei CAI, Suping REN. Survey on anomaly detection algorithms for unmanned aerial vehicle flight data[J]. Journal of Computer Applications, 2023, 43(6): 1833-1841.
祁超帅, 何文思, 焦毅, 马英红, 蔡伟, 任素萍. 无人机飞行数据异常检测算法综述[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1833-1841.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060808
类型 | 优点 | 缺点 |
---|---|---|
基于先验知识的定性算法 | 无需严格的数据定量分析,实现简单 | 先验知识少,异常种类难以完全把握 |
基于模型的定量算法 | 模型准确度较高 | 模型建立困难,移植性差 |
基于数据驱动的算法 | 通用性强,先验知识需求小 | 大多数算法复杂度高,依赖数据的质量 |
Tab. 1 Comparison of pros and cons of anomaly detection algorithms
类型 | 优点 | 缺点 |
---|---|---|
基于先验知识的定性算法 | 无需严格的数据定量分析,实现简单 | 先验知识少,异常种类难以完全把握 |
基于模型的定量算法 | 模型准确度较高 | 模型建立困难,移植性差 |
基于数据驱动的算法 | 通用性强,先验知识需求小 | 大多数算法复杂度高,依赖数据的质量 |
算法类型 | 基本思想 | 优点 | 缺点 |
---|---|---|---|
基于统计 | 异常点分布在低概率区域 | 数据处理速度快,能有效识别单维数据的离群点 | 很难确定概率分布假设,不适用于高维数据 |
基于分类 | 训练决策边界分离异常数据 | 适用于高维且标签明确的场景 | 过于依赖数据标签 |
基于相似性 | 根据数据间的相似度识别异常数据 | 检测准确度高 | 大多相似性算法计算复杂度高 |
基于预测 | 通过数据与预测值的残差判断异常 | 可较好地拟合数据的趋势性 | 对噪声数据敏感 |
Tab. 2 Characteristics of different data-driven algorithms
算法类型 | 基本思想 | 优点 | 缺点 |
---|---|---|---|
基于统计 | 异常点分布在低概率区域 | 数据处理速度快,能有效识别单维数据的离群点 | 很难确定概率分布假设,不适用于高维数据 |
基于分类 | 训练决策边界分离异常数据 | 适用于高维且标签明确的场景 | 过于依赖数据标签 |
基于相似性 | 根据数据间的相似度识别异常数据 | 检测准确度高 | 大多相似性算法计算复杂度高 |
基于预测 | 通过数据与预测值的残差判断异常 | 可较好地拟合数据的趋势性 | 对噪声数据敏感 |
1 | 祁圣君,井立,王亚龙. 无人机系统及发展趋势综述[J]. 飞航导弹, 2018(4):17-21. 10.21884/ijmter.2017.4120.y54mr |
QI S J, JING L, WANG Y L. Overview of unmanned aerial vehicle systems and development trends[J]. Aerodynamic Missile Journal, 2018(4): 17-21. 10.21884/ijmter.2017.4120.y54mr | |
2 | 李磊,王彤,蒋琪. 从美军2042年无人系统路线图看无人系统关键技术发展动向[J]. 无人系统技术, 2018, 1(4):79-84. |
LI L, WANG T, JIANG Q. Key technology develop trends of unmanned systems viewed from unmanned systems integrated roadmap 2017-2042 [J]. Unmanned Systems Technology, 2018, 1(4): 79-84. | |
3 | CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection: a survey [J]. ACM Computing Surveys, 2009, 41(3): No.15. 10.1145/1541880.1541882 |
4 | 陈亚锋. 无人机飞行数据实时异常检测系统研制[D]. 哈尔滨:哈尔滨工业大学, 2017:3-3. 10.1109/phm.2017.8079160 |
CHEN Y F. Real-time anomaly detection system for unmanned aerial vehicle flight data[D]. Harbin: Harbin Institute of Technology, 2017: 3-3. 10.1109/phm.2017.8079160 | |
5 | 彭喜元,庞景月,彭宇,等. 航天器遥测数据异常检测综述[J]. 仪器仪表学报, 2016, 37(9):1929-1945. 10.3969/j.issn.0254-3087.2016.09.003 |
PENG X Y, PANG J Y, PENG Y, et al. Review on anomaly detection of spacecraft telemetry data [J]. Chinese Journal of Scientific Instrument, 2016, 37(9): 1929-1945. 10.3969/j.issn.0254-3087.2016.09.003 | |
6 | LU H M, LI Y J, MU S L, et al. Motor anomaly detection for unmanned aerial vehicles using reinforcement learning [J]. IEEE Internet of Things Journal, 2018, 5(4): 2315-2322. 10.1109/jiot.2017.2737479 |
7 | 黄可鸣. 专家系统导论[M]. 南京:东南大学出版社, 1988: 217-229. |
HUANG K M. An Introduction to Expert Systems[M]. Nanjing: Southeast University Press, 1988: 217-229. | |
8 | 孙学初. 无人机飞控系统故障诊断专家系统设计[D]. 成都:电子科技大学, 2012:14-24. 10.3969/j.issn.1672-545X.2012.02.023 |
SUN X C. Design of UAV flight control system fault diagnosis expert system[D]. Chengdu: University of Electronic Science and Technology of China, 2012:14-24. 10.3969/j.issn.1672-545X.2012.02.023 | |
9 | 刘瀚泽. 基于机器学习的无人机飞控故障智能诊断系统研究[D]. 成都:电子科技大学, 2019:20-25. |
LIU H Z. Research on intelligent diagnosis system of UAV flight control fault based on machine learning [D]. Chengdu: University of Electronic Science and Technology of China, 2019: 20-25. | |
10 | 卿立勇. 基于飞行数据的飞机故障预测与故障诊断系统研究[D]. 南京:南京航空航天大学, 2007:30-50. 10.7666/d.d037712 |
QING L Y. Research on airplane fault prognosis and diagnosis system based on flight data [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007: 30-50. 10.7666/d.d037712 | |
11 | 北京航空航天大学. 一种概率耦合关系下无人机系统多层级风险评估方法: 202010410327.1[P]. 2020-09-18. |
Beihang University. A multi-level risk assessment method for unmanned aerial vehicle system under probabilistic coupling relationship: 202010410327.1 [P]. 2020-09-18. | |
12 | GAO Z W, CECATI C, DING S X. A survey of fault diagnosis and fault-tolerant techniques — Part I: fault diagnosis with model-based and signal-based approaches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757-3767. 10.1109/tie.2015.2417501 |
13 | CHEN M J, PAN Z, CHI C Z, et al. Research on UAV wing structure health monitoring technology based on finite element simulation analysis[C]// Proceedings of the 11th International Conference on Prognostics and System Health Management. Piscataway: IEEE, 2020: 86-90. 10.1109/phm-jinan48558.2020.00022 |
14 | BEARD R V. Failure accommodation in linear systems through self-reorganization [R/OL]. [2022-03-21].. |
15 | 谈娟. 基于解析模型的飞控系统故障诊断技术研究[D]. 南京:南京航空航天大学, 2020:12-15. |
TAN J. Research on fault diagnosis technology of flight control system based on analytical model[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020: 12-15. | |
16 | 刘哲成,郭丽娟. 基于分层滤波算法的无人机控制系统故障检测技术[J]. 计算机测量与控制, 2020, 28(5):23-26, 30. |
LIU Z C, GUO L J. Fault detection technology for UAV control system based on hierarchical filtering algorithm[J]. Computer Measurement and Control, 2020, 28(5): 23-26, 30. | |
17 | 武宝军. 无人机飞行控制系统故障检测技术研究[D]. 西安:西北工业大学, 2007:21-31. |
WU B J. Research on fault detection technology of UAV flight control system [D]. Xi’an: Northwestern Polytechnical University, 2007: 21-31. | |
18 | 贾庆贤,张迎春,管宇,等. 基于解析模型的非线性系统故障诊断方法综述[J]. 信息与控制, 2012, 41(3):356-364. |
JIA Q X, ZHANG Y C, GUAN Y, et al. Fault diagnosis of nonlinear systems based on analytical models: a survey [J]. Information and Control, 2012, 41(3): 356-364. | |
19 | 刘剑慰. 基于模型的飞行控制系统故障诊断方法研究[D]. 南京:南京航空航天大学, 2014:23-78. |
LIU J W. Research of model based fault diagnosis for flight control systems[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014:23-78. | |
20 | MELNYK I, MATTHEWS B, VALIZADEGAN H, et al. Vector autoregressive model-based anomaly detection in aviation systems[J]. Journal of Aerospace Information Systems, 2016, 13(4): 161-173. 10.2514/1.i010394 |
21 | PASCHALIDIS I C, CHEN Y. Statistical anomaly detection with sensor networks[J]. ACM Transactions on Sensor Networks, 2010, 7(2): No.17. 10.1145/1824766.1824773 |
22 | FREEMAN P, PANDITA R, SRIVASTAVA N, et al. Model-based and data-driven fault detection performance for a small UAV[J]. IEEE/ASME Transactions on Mechatronics, 2013, 18(4): 1300-1309. 10.1109/tmech.2013.2258678 |
23 | MELNYK I, YADAV P, STEINBACH M, et al. Detection of precursors to aviation safety incidents due to human factors [C]// Proceedings of the IEEE 13th International Conference on Data Mining Workshops. Piscataway: IEEE, 2013: 407-412. 10.1109/icdmw.2013.55 |
24 | CHOWDHARY G, SRINIVASAN S, JOHNSON E N. Frequency domain method for real-time detection of oscillations [J]. Journal of Aerospace Computing, Information, and Communication, 2011, 8(2): 42-52. 10.2514/1.52110 |
25 | BRONZ M, BASKAYA E, DELAHAYE D, et al. Real-time fault detection on small fixed-wing UAVs using machine learning [C]// Proceedings of the IEEE/AIAA 39th Digital Avionics Systems Conference. Piscataway: IEEE, 2020: 1-10. 10.1109/dasc50938.2020.9256800 |
26 | YAMAN O, YOL F, ALTINORS A. A fault detection method based on embedded feature extraction and SVM classification for UAV motors [J]. Microprocessors and Microsystems, 2022, 94: No.104683. 10.1016/j.micpro.2022.104683 |
27 | DAS S, MATTHEWS B L, SRIVASTAVA A N, et al. Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study [C]// Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010: 47-56. 10.1145/1835804.1835813 |
28 | PURANIK T G, MAVRIS D N. Anomaly detection in general-aviation operations using energy metrics and flight-data records[J]. Journal of Aerospace Information Systems, 2018, 15(1): 22-36. 10.2514/1.i010582 |
29 | PAN D W. Hybrid data-driven anomaly detection method to improve UAV operating reliability [C]// Proceedings of the 2017 Prognostics and System Health Management Conference. Piscataway: IEEE, 2017: 1-4. 10.1109/phm.2017.8079281 |
30 | PAN D W, NIE L Q, KANG W X, et al. UAV anomaly detection using active learning and improved S3VM model [C]// Proceedings of the 2020 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence. Piscataway: IEEE, 2020: 253-258. 10.1109/icsmd50554.2020.9261709 |
31 | MACK D L C, BISWAS G, KOUTSOUKOS X D, et al. Learning Bayesian network structures to augment aircraft diagnostic reference models[J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 358-369. 10.1109/tase.2016.2542186 |
32 | LIU F T, TING K M, ZHOU Z H. Isolation-based anomaly detection[J]. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): No.3. 10.1145/2133360.2133363 |
33 | YU L, DING W R. A KNNS based anomaly detection method applied for UAV flight data stream [C]// Proceedings of the 2015 Prognostics and System Health Management Conference. Piscataway: IEEE, 2015:1-8. 10.1109/phm.2015.7380051 |
34 | LI L S, DAS S, HANSMAN R J, et al. Analysis of flight data using clustering techniques for detecting abnormal operations [J]. Journal of Aerospace Information Systems, 2015, 12(9): 587-598. 10.2514/1.i010329 |
35 | KHAN S, LIEW C F, YAIRI T, et al. Unsupervised anomaly detection in unmanned aerial vehicles[J]. Applied Soft Computing, 2019, 83: No.105650. 10.1016/j.asoc.2019.105650 |
36 | 陈圣楠,钱红燕,李伟. 基于角度方差的多层次高维数据异常检测算法[J]. 计算机应用研究, 2016, 33(11):3383-3386. |
CHEN S N, QIAN H Y, LI W. Hybrid outlier detection algorithm based on angle variance for high-dimensional data [J]. Application Research of Computers, 2016, 33(11): 3383-3386. | |
37 | KRIEGEL H P, SCHUBERT M, ZIMEK A. Angle-based outlier detection in high-dimensional data [C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008: 444-452. 10.1145/1401890.1401946 |
38 | 何永福. 基于子空间学习的无人机飞行数据瞬时异常检测研究[D]. 哈尔滨:哈尔滨工业大学, 2019:50-70. |
HE Y F. UAV flight data instantaneous anomalies detection based on subspace learning[D]. Harbin: Harbin Institute of Technology, 2019:50-70. | |
39 | WANG B K, LIU D T, PENG X Y, et al. Data-driven anomaly detection of UAV based on multimodal regression model[C]// Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference. Piscataway: IEEE, 2019: 1-6. 10.1109/i2mtc.2019.8827154 |
40 | WANG B K, WANG Z Y, LIU L S, et al. Data-driven anomaly detection for UAV sensor data based on deep learning prediction model[C]// Proceedings of the 2019 Prognostics and System Health Management Conference. Piscataway: IEEE, 2019: 286-290. 10.1109/phm-paris.2019.00055 |
41 | 王泽洋. 基于LSTM的无人机异常检测方法研究[D]. 哈尔滨:哈尔滨工业大学, 2018:15-44. |
WANG Z Y. Unmanned aerial vehicle anomaly detection based on long short term memory [D]. Harbin: Harbin Institute of Technology, 2018: 15-44. | |
42 | ALOS A, DAHROUJ Z. Using MLSTM and multioutput convolutional LSTM algorithms for detecting anomalous patterns in streamed data of unmanned aerial vehicles[J]. IEEE Aerospace and Electronic Systems Magazine, 2022, 37(6): 6-15. 10.1109/maes.2021.3053108 |
43 | ZHONG J, ZHANG Y J, WANG J Y, et al. Unmanned aerial vehicle flight data anomaly detection and recovery prediction based on spatio-temporal correlation[J]. IEEE Transactions on Reliability, 2022, 71(1): 457-468. 10.1109/tr.2021.3134369 |
44 | DUDUKCU H V, TASKIRAN M, KAHRAMAN N. Unmanned Aerial Vehicles (UAVs) battery power anomaly detection using temporal convolutional network with simple moving average algorithm[C]// Proceedings of the 2022 International Conference on Innovations in Intelligent Systems and Applications. Piscataway: IEEE, 2022: 1-5. 10.1109/inista55318.2022.9894193 |
45 | YOU J T, LIANG J, LIU D T. An adaptable UAV sensor data anomaly detection method based on TCN model transferring [C]// Proceedings of the 2022 Prognostics and Health Management Conference. Piscataway: IEEE, 2022:73-76. 10.1109/phm2022-london52454.2022.00021 |
46 | CHEN Y F, WANG B K, LIU W, et al. On-line and non-invasive anomaly detection system for unmanned aerial vehicle[C]// Proceedings of 2017 Prognostics and System Health Management Conference. Piscataway: IEEE, 2017: 1-7. 10.1109/phm.2017.8079160 |
47 | 李晨,王布宏,田继伟,等. 基于LSTM-OCSVM的无人机传感器数据异常检测[J]. 小型微型计算机系统, 2021, 42(4):700-705. 10.3969/j.issn.1000-1220.2021.04.005 |
LI C, WANG B H, TIAN J W, et al. Anomaly detection method for UAV sensor data based on LSTM-OCSVM [J]. Journal of Chinese Computer Systems, 2021, 42(4): 700-705. 10.3969/j.issn.1000-1220.2021.04.005 | |
48 | PANG G S, SHEN C H, CAO L B, et al. Deep learning for anomaly detection: a review [J]. ACM Computing Surveys, 2022, 54(2): No.38. 10.1145/3439950 |
[1] | Tingwei CHEN, Jiacheng ZHANG, Junlu WANG. Random validation blockchain construction for federated learning [J]. Journal of Computer Applications, 2024, 44(9): 2770-2776. |
[2] | Lingxia MU, Zhengjun ZHOU, Ban WANG, Youmin ZHANG, Xianghong XUE, Kaikai NING. Formation obstacle-avoidance and reconfiguration method for multiple UAVs [J]. Journal of Computer Applications, 2024, 44(9): 2938-2946. |
[3] | Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499. |
[4] | Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557. |
[5] | Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072. |
[6] | Yao DONG, Yixue FU, Yongfeng DONG, Jin SHI, Chen CHEN. Survey of incomplete multi-view clustering [J]. Journal of Computer Applications, 2024, 44(6): 1673-1682. |
[7] | Xinrui LIN, Xiaofei WANG, Yan ZHU. Academic anomaly citation group detection based on local extended community detection [J]. Journal of Computer Applications, 2024, 44(6): 1855-1861. |
[8] | Fan MENG, Qunli YANG, Jing HUO, Xinkuan WANG. EraseMTS: iterative active multivariable time series anomaly detection algorithm based on margin anomaly candidate set [J]. Journal of Computer Applications, 2024, 44(5): 1458-1463. |
[9] | Zhiqiang ZHENG, Haibin DUAN. Short-range UAV air combat maneuver decision-making via finite tolerance pigeon-inspired optimization [J]. Journal of Computer Applications, 2024, 44(5): 1401-1407. |
[10] | Tianyu HUANG, Yuanxing LI, Hao CHEN, Zijia GUO, Mingjun WEI. User cluster partitioning method based on weighted fuzzy clustering in ground-air collaboration scenarios [J]. Journal of Computer Applications, 2024, 44(5): 1555-1561. |
[11] | Zimeng ZHU, Zhixin LI, Zhan HUAN, Ying CHEN, Jiuzhen LIANG. Weakly supervised video anomaly detection based on triplet-centered guidance [J]. Journal of Computer Applications, 2024, 44(5): 1452-1457. |
[12] | Rui TANG, Shibo YUE, Ruizhi ZHANG, Chuan LIU, Chuanlin PANG. Energy efficiency optimization mechanism for UAV-assisted and non-orthogonal multiple access-enabled data collection system [J]. Journal of Computer Applications, 2024, 44(4): 1209-1218. |
[13] | Meiyu CAI, Runzhe ZHU, Fei WU, Kaiyu ZHANG, Jiale LI. Cross-view matching model based on attention mechanism and multi-granularity feature fusion [J]. Journal of Computer Applications, 2024, 44(3): 901-908. |
[14] | Keshuai YANG, Youxi WU, Meng GENG, Jingyu LIU, Yan LI. Top-k high average utility sequential pattern mining algorithm under one-off condition [J]. Journal of Computer Applications, 2024, 44(2): 477-484. |
[15] | Huzhen GAO, Changping DU, Yao ZHENG. Gimbal system control algorithm of unmanned aerial vehicle based on extended state observer [J]. Journal of Computer Applications, 2024, 44(2): 604-610. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||