飞行数据异常检测技术综述
|
摘要
飞行数据是一系列与飞行和运行状态相关的参数。飞行数据异常检测技术旨在监测航空器关键部件的健康状态、发现机组飞行操纵等问题,从而有利于维修维护、消除安全隐患和确保飞行安全。但是,标签数据的缺少、高准确率要求、计算资源限制等问题为实际应用带来严峻挑战。阐述了飞行数据异常检测的基本内涵和研究现状,并在此基础上探讨了潜在的问题和可能的发展方向,力求为飞行数据异常检测技术的发展提供可行的研究思路。
Flight data are series of parameters related to flight and operation status. The flight data anomaly detection technique is aimed at monitoring the health status of key components of the aircraft and discovering the crew flight control problems, which benefits to the daily maintenance, eliminating potential safety hazard and ensuring aviation safety. However, the lack of labeled data, high accuracy requirement, computing resource constraint and other issues also pose serious challenges to practical use. This paper describes the basic connotation and research status of flight data anomaly detection, and on this basis, discusses the potential problems and possible development directions, and strives hard to provide feasible research ideas for the development of flight data anomaly detection.
Flight data are series of parameters related to flight and operation status. The flight data anomaly detection technique is aimed at monitoring the health status of key components of the aircraft and discovering the crew flight control problems, which benefits to the daily maintenance, eliminating potential safety hazard and ensuring aviation safety. However, the lack of labeled data, high accuracy requirement, computing resource constraint and other issues also pose serious challenges to practical use. This paper describes the basic connotation and research status of flight data anomaly detection, and on this basis, discusses the potential problems and possible development directions, and strives hard to provide feasible research ideas for the development of flight data anomaly detection.
引文
[1] BUDALAKOTI S,SRIVASTAVA A N,OTEY M E.Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety[J].IEEE Transactions on System,Man,And Cybernetics Part C,Application and Reviews,2009,39(1):101- 113.
[2] QI X,THEILLIOL D,QI J,et al,A literature review on fault diagnosis methods for manned and unmanned helicopters[C].IEEE International Conference on Unmanned Aircraft Systems,2013:1114- 1118.
[3] KHALASTCHI E,KALECH M.On fault detection and diagnosis in robotic systems[J].ACM Computing Surveys (CSUR),2018,51(1):1- 24.
[4] MATTHEWS B,DAS S,BHADURI K,et al.Discovering anomalous aviation safety events using scalable data mining algorithms[J].Journal of Aerospace Information Systems,2013,10(10):467- 475.
[5] MORALES M A,HAAS D J.Self-monitoring activities for autonomous fight data analysis[J].Journal of Aircraft,2012,49(5):1245- 1254.
[6] MOOSBRUGGER P,ROZIER K Y,SCHUMANN J.R2U2:Monitoring and diagnosis of security threats for unmanned aerial systems[J].Formal Methods in System Design,2017,51(1) :31- 61.
[7] CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detection:A survey[J].ACM Computing Surveys,2009,41(3):15- 45.
[8] CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detection for discrete sequences:A survey[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(5):823- 839.
[9] GUPTA M,GAO J,AGGARWAL C C,et al.Outlier detection for temporal data:A survey[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(9):2250- 2267.
[10] AKOGLU L,TONG H,KOUTRA D.Graph based anomaly detection and description:A survey[J].Data Mining and Knowledge Discovery,2015,29(3):626- 688.
[11] 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.
[12] VAIDYA A,LEE S,HWANG I.Data-driven modeling and analysis framework for cockpit human-machine interaction issues[J].Journal of Aerospace Information Systems,2016,13(9):370- 380.
[13] HANSEN S,BLANKE M.Diagnosis of airspeed measurement faults for unmanned aerial vehicles[J].IEEE Transactions on Aerospace and Electronic Systems,2014,50(1):224- 239.
[14] MELNYK I,BANERJEE A,MATTHEWS B,et al.Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016:1065- 1074.
[15] LI L,HANSMAN R J,PALACIOS R,et al.Anomaly detection via a Gaussian mixture model for flight operation and safety monitoring[J].Transportation Research Part C:Emerging Technologies,2016,64:45- 57.
[16] PURANIK T G,MAVRIS D N.Identifying instantaneous anomalies in general aviation operations[C].17th AIAA Aviation Technology,Integration,and Operations Conference,2017:3779- 3794.
[17] SCHUMANN J,ROZIER K Y,REINBACHER T,et al.Towards real-time,on-board,hardware-supported sensor and software health management for unmanned aerial systems[J].International Journal of Prognostics and Health Management,2015,6(1):1- 27.
[18] 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,2010:47- 56.
[19] SMART E,BROWN D,DENMAN J.Combining multiple classifiers to quantitatively rank the impact of abnormalities in flight data[J].Applied Soft Computing,2012,12(8):2583- 2592.
[20] 孙文柱,曲建岭,袁涛,等.基于改进 SVDD 的飞参数据新异检测方法[J].仪器仪表学报,2014,35(4):932- 939.SUN W Z,QU J L,YUAN T,et al.Flight data novelty detection method based on improved SVDD[J].Chinese Journal of Scientific Instrument,2014,35(4):932- 939.
[21] JANAKIRAMAN V M,MATTHEWS B,OZA N.Finding precursors to anomalous drop in airspeed during a flight's takeoff[C].Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2017:1843- 1852.
[22] DAS S,MATTHEWS B L,LAWRENCE R.Fleet level anomaly detection of aviation safety data[C].IEEE Conference on Prognostics and Health Management,2011:1- 10.
[23] LI L,DAS S,JOHN HANSMAN R,et al.Analysis of flight data using clustering techniques for detecting abnormal operations[J].Journal of Aerospace information systems,2015,12(9):587- 598.
[24] CHOWDHARY G V,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.
[25] MELNYK I,YADAV P,STEINBACH M,et al.Detection of precursors to aviation safety incidents due to human factors[C].13th IEEE International Conference on Data Mining Workshops,2013:407- 412.
[26] KHALASTCHI E,KALECH M,KAMINKA G A,et al.Online data-driven anomaly detection in autonomous robots[J].Knowledge and Information Systems,2015,43(3):657- 688.
[27] 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.
[28] BIRNBAUM Z,DOLGIKH A,SKORMIN V,et al.Unmanned aerial vehicle security using recursive parameter estimation[J].Journal of Intelligent & Robotic Systems,2016,84(1- 4):107- 120.
[29] BREUNIG M M,KRIEGEL H P,NG R T,et al.LOF:identifying density-based local outliers[C].ACM SIGMOD International Conference on Management of Data,2000:93- 104.
[30] PURANIK T G.,JIMENEZ H,MAVRIS D N Utilizing energy metrics and clustering techniques to identify anomalous general aviation operations[C].AIAA SciTech Forum,2017:0789- 0808.
[31] MANUKYAN A,OLIVARES-MENDEZ M A,VOOS H,et al.Real time degradation identification of UAV using machine learning techniques[C].IEEE International Conference on Unmanned Aircraft Systems,2017:1223- 1230.
[32] KRIEGEL H P,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,2008:444- 452.
[33] KEOGH E,LIN J,FU A.HOT SAX:Efficiently finding the most unusual time series subsequence[C].5th IEEE International Conference on Data Mining,2005:226- 233.
[34] 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.
[35] PURANIK T,JIMENEZ H,MAVRIS D.Energy-based metrics for safety analysis of general aviation operations[J].Journal of Aircraft,2017,54(6):2285- 2297.
[36] BENGIO Y,COURVILLE A,VINCENT P.Representation learning:A review and new perspectives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1798- 1828.
[37] GONEN A,ROSENBAUM D,ELDAR Y C,et al.Subspace learning with partial information[J].The Journal of Machine Learning Research,2016,17(1):1821- 1841.
[38] ZHANG Z,LUO P,LOY C C,et al.Learning deep representation for face alignment with auxiliary attributes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(5):918- 930.
[39] 何永福,王少军,王文娟,等.基于过采样投影近似基追踪的无人机异常检测[J].仪器仪表学报,2016,37(7):1468- 1476.HE Y F,WANG SH J,WANG W J,UAV anomaly detection based on oversampling projection approximation basis pursuit [J].Chinese Journal of Scientific Instrument,2016,37(7):1468- 1476.
[40] DAS S,BHADURI K,OZA N C,et al.ν-anomica:fast support vector based novelty detection technique[C].Ninth IEEE International Conference on Data Mining,2009:101- 109.
[41] IVERSON D.Data mining applications for space mission operations system health monitoring [C].Proceedings of the AIAA SpaceOps Conference,2008:1- 8.
[42] BAY S D,SCHWABACHE R M.Mining distance-based outliers in near linear time with randomization and a simple pruning rule [C].ACM SIGKDD International conference on Knowledge Discovery and Data Mining,2003:29- 38.
[43] JANAKIRAMAN V M.Explaining aviation safety incidents using deep temporal multiple instance learning[C].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2018:406- 415.
[44] ZHANG C,WANG N.Aero-engine condition monitoring based on support vector machine[J].Physics Procedia,2012:1546- 1552.
[45] PANG Y,WANG S,PENG Y,et al.A microcoded kernel recursive least squares processor using fpga technology[J].ACM Transactions on Reconfigurable Technology and Systems,2016,10(1):5.
[46] MIKOLOV T,CORRADO G.,CHEN K.,et al.Efficient estimation of word representations in vector space[C].Proceedings of the International Conference on Learning Representations,2013:1-12.
[47] PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on knowledge and data engineering,2010,22(10):1345- 1359.
[48] YING W,ZHANG Y,HUANG J,et al.Transfer learning via learning to transfer[C].In Proceedings of 35th International Conference on Machine Learning,2018:5072- 5081.
[49] SHERVASHIDZE N,BACH F.Learning the structure for structured sparsity[J].IEEE Transactions on Signal Processing,2015,63(18):4894- 4902.
[50] RIBEIRO M T,SINGH S,GUESTRIN C.Explaining the predictions of any classifier[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016:1135- 1144.
[51] ZHANG Q,ZHU S C.Visual interpretability for deep learning:A survey[J].Frontiers of Information Technology & Electronic Engineering,2018,19(1):27- 39.
[52] SZE V,CHEN Y H,EMER J,et al.Hardware for machine learning:Challenges and opportunities[C].IEEE Custom Integrated Circuits Conference,2017:1- 8.
[53] GUI J,SUN Z,JI S,et al.Feature selection based on structured sparsity:A comprehensive study[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(7):1490- 1507.
[54] CHENG Y,WANG D,ZHOU P,et al.Model compression and acceleration for deep neural networks:The principles,progress,and challenges[J].IEEE Signal Processing Magazine,2018,35(1):126- 136.
[55] MENG X,BRADLEY J,YAVUZ B,et al.Mllib:Machine learning in apache spark[J].The Journal of Machine Learning Research,2016,17(1):1235- 1241.
[56] XING E P,HO Q,XIE P,et al.Strategies and principles of distributed machine learning on big data[J].Engineering,2016,2(2):179- 195.
[2] QI X,THEILLIOL D,QI J,et al,A literature review on fault diagnosis methods for manned and unmanned helicopters[C].IEEE International Conference on Unmanned Aircraft Systems,2013:1114- 1118.
[3] KHALASTCHI E,KALECH M.On fault detection and diagnosis in robotic systems[J].ACM Computing Surveys (CSUR),2018,51(1):1- 24.
[4] MATTHEWS B,DAS S,BHADURI K,et al.Discovering anomalous aviation safety events using scalable data mining algorithms[J].Journal of Aerospace Information Systems,2013,10(10):467- 475.
[5] MORALES M A,HAAS D J.Self-monitoring activities for autonomous fight data analysis[J].Journal of Aircraft,2012,49(5):1245- 1254.
[6] MOOSBRUGGER P,ROZIER K Y,SCHUMANN J.R2U2:Monitoring and diagnosis of security threats for unmanned aerial systems[J].Formal Methods in System Design,2017,51(1) :31- 61.
[7] CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detection:A survey[J].ACM Computing Surveys,2009,41(3):15- 45.
[8] CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detection for discrete sequences:A survey[J].IEEE Transactions on Knowledge and Data Engineering,2012,24(5):823- 839.
[9] GUPTA M,GAO J,AGGARWAL C C,et al.Outlier detection for temporal data:A survey[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(9):2250- 2267.
[10] AKOGLU L,TONG H,KOUTRA D.Graph based anomaly detection and description:A survey[J].Data Mining and Knowledge Discovery,2015,29(3):626- 688.
[11] 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.
[12] VAIDYA A,LEE S,HWANG I.Data-driven modeling and analysis framework for cockpit human-machine interaction issues[J].Journal of Aerospace Information Systems,2016,13(9):370- 380.
[13] HANSEN S,BLANKE M.Diagnosis of airspeed measurement faults for unmanned aerial vehicles[J].IEEE Transactions on Aerospace and Electronic Systems,2014,50(1):224- 239.
[14] MELNYK I,BANERJEE A,MATTHEWS B,et al.Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016:1065- 1074.
[15] LI L,HANSMAN R J,PALACIOS R,et al.Anomaly detection via a Gaussian mixture model for flight operation and safety monitoring[J].Transportation Research Part C:Emerging Technologies,2016,64:45- 57.
[16] PURANIK T G,MAVRIS D N.Identifying instantaneous anomalies in general aviation operations[C].17th AIAA Aviation Technology,Integration,and Operations Conference,2017:3779- 3794.
[17] SCHUMANN J,ROZIER K Y,REINBACHER T,et al.Towards real-time,on-board,hardware-supported sensor and software health management for unmanned aerial systems[J].International Journal of Prognostics and Health Management,2015,6(1):1- 27.
[18] 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,2010:47- 56.
[19] SMART E,BROWN D,DENMAN J.Combining multiple classifiers to quantitatively rank the impact of abnormalities in flight data[J].Applied Soft Computing,2012,12(8):2583- 2592.
[20] 孙文柱,曲建岭,袁涛,等.基于改进 SVDD 的飞参数据新异检测方法[J].仪器仪表学报,2014,35(4):932- 939.SUN W Z,QU J L,YUAN T,et al.Flight data novelty detection method based on improved SVDD[J].Chinese Journal of Scientific Instrument,2014,35(4):932- 939.
[21] JANAKIRAMAN V M,MATTHEWS B,OZA N.Finding precursors to anomalous drop in airspeed during a flight's takeoff[C].Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2017:1843- 1852.
[22] DAS S,MATTHEWS B L,LAWRENCE R.Fleet level anomaly detection of aviation safety data[C].IEEE Conference on Prognostics and Health Management,2011:1- 10.
[23] LI L,DAS S,JOHN HANSMAN R,et al.Analysis of flight data using clustering techniques for detecting abnormal operations[J].Journal of Aerospace information systems,2015,12(9):587- 598.
[24] CHOWDHARY G V,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.
[25] MELNYK I,YADAV P,STEINBACH M,et al.Detection of precursors to aviation safety incidents due to human factors[C].13th IEEE International Conference on Data Mining Workshops,2013:407- 412.
[26] KHALASTCHI E,KALECH M,KAMINKA G A,et al.Online data-driven anomaly detection in autonomous robots[J].Knowledge and Information Systems,2015,43(3):657- 688.
[27] 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.
[28] BIRNBAUM Z,DOLGIKH A,SKORMIN V,et al.Unmanned aerial vehicle security using recursive parameter estimation[J].Journal of Intelligent & Robotic Systems,2016,84(1- 4):107- 120.
[29] BREUNIG M M,KRIEGEL H P,NG R T,et al.LOF:identifying density-based local outliers[C].ACM SIGMOD International Conference on Management of Data,2000:93- 104.
[30] PURANIK T G.,JIMENEZ H,MAVRIS D N Utilizing energy metrics and clustering techniques to identify anomalous general aviation operations[C].AIAA SciTech Forum,2017:0789- 0808.
[31] MANUKYAN A,OLIVARES-MENDEZ M A,VOOS H,et al.Real time degradation identification of UAV using machine learning techniques[C].IEEE International Conference on Unmanned Aircraft Systems,2017:1223- 1230.
[32] KRIEGEL H P,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,2008:444- 452.
[33] KEOGH E,LIN J,FU A.HOT SAX:Efficiently finding the most unusual time series subsequence[C].5th IEEE International Conference on Data Mining,2005:226- 233.
[34] 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.
[35] PURANIK T,JIMENEZ H,MAVRIS D.Energy-based metrics for safety analysis of general aviation operations[J].Journal of Aircraft,2017,54(6):2285- 2297.
[36] BENGIO Y,COURVILLE A,VINCENT P.Representation learning:A review and new perspectives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1798- 1828.
[37] GONEN A,ROSENBAUM D,ELDAR Y C,et al.Subspace learning with partial information[J].The Journal of Machine Learning Research,2016,17(1):1821- 1841.
[38] ZHANG Z,LUO P,LOY C C,et al.Learning deep representation for face alignment with auxiliary attributes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(5):918- 930.
[39] 何永福,王少军,王文娟,等.基于过采样投影近似基追踪的无人机异常检测[J].仪器仪表学报,2016,37(7):1468- 1476.HE Y F,WANG SH J,WANG W J,UAV anomaly detection based on oversampling projection approximation basis pursuit [J].Chinese Journal of Scientific Instrument,2016,37(7):1468- 1476.
[40] DAS S,BHADURI K,OZA N C,et al.ν-anomica:fast support vector based novelty detection technique[C].Ninth IEEE International Conference on Data Mining,2009:101- 109.
[41] IVERSON D.Data mining applications for space mission operations system health monitoring [C].Proceedings of the AIAA SpaceOps Conference,2008:1- 8.
[42] BAY S D,SCHWABACHE R M.Mining distance-based outliers in near linear time with randomization and a simple pruning rule [C].ACM SIGKDD International conference on Knowledge Discovery and Data Mining,2003:29- 38.
[43] JANAKIRAMAN V M.Explaining aviation safety incidents using deep temporal multiple instance learning[C].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,2018:406- 415.
[44] ZHANG C,WANG N.Aero-engine condition monitoring based on support vector machine[J].Physics Procedia,2012:1546- 1552.
[45] PANG Y,WANG S,PENG Y,et al.A microcoded kernel recursive least squares processor using fpga technology[J].ACM Transactions on Reconfigurable Technology and Systems,2016,10(1):5.
[46] MIKOLOV T,CORRADO G.,CHEN K.,et al.Efficient estimation of word representations in vector space[C].Proceedings of the International Conference on Learning Representations,2013:1-12.
[47] PAN S J,YANG Q.A survey on transfer learning[J].IEEE Transactions on knowledge and data engineering,2010,22(10):1345- 1359.
[48] YING W,ZHANG Y,HUANG J,et al.Transfer learning via learning to transfer[C].In Proceedings of 35th International Conference on Machine Learning,2018:5072- 5081.
[49] SHERVASHIDZE N,BACH F.Learning the structure for structured sparsity[J].IEEE Transactions on Signal Processing,2015,63(18):4894- 4902.
[50] RIBEIRO M T,SINGH S,GUESTRIN C.Explaining the predictions of any classifier[C].Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2016:1135- 1144.
[51] ZHANG Q,ZHU S C.Visual interpretability for deep learning:A survey[J].Frontiers of Information Technology & Electronic Engineering,2018,19(1):27- 39.
[52] SZE V,CHEN Y H,EMER J,et al.Hardware for machine learning:Challenges and opportunities[C].IEEE Custom Integrated Circuits Conference,2017:1- 8.
[53] GUI J,SUN Z,JI S,et al.Feature selection based on structured sparsity:A comprehensive study[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(7):1490- 1507.
[54] CHENG Y,WANG D,ZHOU P,et al.Model compression and acceleration for deep neural networks:The principles,progress,and challenges[J].IEEE Signal Processing Magazine,2018,35(1):126- 136.
[55] MENG X,BRADLEY J,YAVUZ B,et al.Mllib:Machine learning in apache spark[J].The Journal of Machine Learning Research,2016,17(1):1235- 1241.
[56] XING E P,HO Q,XIE P,et al.Strategies and principles of distributed machine learning on big data[J].Engineering,2016,2(2):179- 195.