A. Kataria and M. D. Singh, A Review of Data Classification Using K-Nearest Neighbour Algorithm, International Journal of Emerging Technology and Advanced Engineering, vol.36, pp.354-360, 2013.

L. Raz, E. Khalastchi, and G. Kaminka, Detecting anomalies in unmanned vehicles using the mahalanobis distance, 2010 IEEE International Conference on

D. Stavrou, Fault detection for service mobile robots using model-based method, Autonomous Robots, vol.47, issue.4, pp.383-394, 2016.
DOI : 10.1109/ROBOT.2005.1570640

C. Plagemann, C. Stachniss, and W. Burgard, Efficient Failure Detection for Mobile Robots Using Mixed-Abstraction Particle Filters, European Robotics Symposium, 2006.
DOI : 10.1007/11681120_8

J. I. Larrauri, G. Sorrosal, and M. Gonzlez, Automatic system for overhead power line inspection using an Unmanned Aerial VehicleRELIFO project, Unmanned Aircraft Systems (ICUAS), 2013 International Conference on, 2013.
DOI : 10.1109/icuas.2013.6564696

T. Brotherton and R. Mackey, Anomaly detector fusion processing for advanced military aircraft, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542), 2001.
DOI : 10.1109/AERO.2001.931330
URL : http://www.iac-online.com/images/publications/20.pdf

M. Afridi, A. Jamal, J. Javed-awan, and . Iqbal, AWG-Detector: A machine learning tool for the accurate detection of Anomalies due to Wind Gusts (AWG) in the adaptive Altitude control unit of an Aerosonde unmanned Aerial Vehicle, 2010 10th International Conference on Intelligent Systems Design and Applications, 2010.
DOI : 10.1109/ISDA.2010.5687036

E. Khalastchi, Online anomaly detection in unmanned vehicles, The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 1. International Foundation for Autonomous Agents and Multiagent Systems, 2011.

J. Laurikkala, Informal identification of outliers in medical data, Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology, vol.1, 2000.

D. Pokrajac, A. Lazarevic, and L. J. Latecki, Incremental Local Outlier Detection for Data Streams, 2007 IEEE Symposium on Computational Intelligence and Data Mining, 2007.
DOI : 10.1109/CIDM.2007.368917

R. Laxhammar and G. Falkman, Online Learning and Sequential Anomaly Detection in Trajectories, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.6, pp.1158-1173, 2014.
DOI : 10.1109/TPAMI.2013.172

S. Ahmad and S. Purdy, Real-Time Anomaly Detection for Streaming Analytics, 2016.

Y. Lee, Nearest-neighbor-based approach to time-series classification, Decision Support Systems, vol.53, issue.1, pp.207-217, 2012.
DOI : 10.1016/j.dss.2011.12.014

A. Manukyan, UAV degradation identification for pilot notification using machine learning techniques, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016.
DOI : 10.1109/ETFA.2016.7733537

W. Chaovalitwongse, Y. Art, R. C. Fan, and . Sachdeo, On the Time Series $K$-Nearest Neighbor Classification of Abnormal Brain Activity, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.37, issue.6, pp.1005-1016, 2007.
DOI : 10.1109/TSMCA.2007.897589

N. Suguna and K. Thanushkodi, An improved K-nearest neighbor classification using Genetic Algorithm, International Journal of Computer Science Issues, vol.7, issue.2, pp.18-21, 2010.

Y. Park, C. E. Priebe, and A. Youssef, Anomaly Detection in Time Series of Graphs using Fusion of Graph Invariants, IEEE Journal of Selected Topics in Signal Processing, vol.7, issue.1, pp.67-75, 2013.
DOI : 10.1109/JSTSP.2012.2233712

A. Abid, M. Tahir-khan, and C. W. De-silva, Fault detection in mobile robots using sensor fusion, 2015 10th International Conference on Computer Science & Education (ICCSE), 2015.
DOI : 10.1109/ICCSE.2015.7250209

Z. Duan, H. Ma, and L. Yang, Fault detection for internal sensors of mobile robots based on support vector data description, The 27th Chinese Control and Decision Conference (2015 CCDC), 2015.
DOI : 10.1109/CCDC.2015.7162389

F. Arrichiello, A. Marino, and F. Pierri, A decentralized fault detection and isolation strategy for networked robots, 2013 16th International Conference on Advanced Robotics (ICAR), 2013.
DOI : 10.1109/ICAR.2013.6766562

X. Wu, Top 10 algorithms in data mining [22] iRobot Roomba https://www.irobot.com/For-the-Home/Vacuuming/ Roomba.aspx [23] Video of the UAV Good and Worst flight https, Knowledge and Information Systems, vol.141, pp.1-37, 2008.
DOI : 10.1007/s10115-007-0114-2
URL : http://www.cse.ust.hk/~qyang/Docs/2007/top10.pdf

P. Yang, A tighter lower bound estimate for dynamic time warping Searching and mining trillions of time series subsequences under dynamic time warping, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012.

V. Correia and . Monteiro, The Aircraft Maintenance Program and its importance on Continuing Airworthiness Management