Data-driven detection and diagnosis on the navigation states of surface vessels

The surface vessel as a small surface warship capable of autonomous planning and navigation and mostly used to carry out various dangerous battle missions has been widely used by many countries in military field. First, the advantages and importance of surface vessels are introduced and the necessity of conducting fault detection and diagnosis on the navigation states is discussed as well. Second, the basic theories of three data-driven approaches are presented, including PCA based approach, SVM based approach and BPNN based approach. Third, we analyze the results of the three data-driven approaches and propose the accessible methods of detection and diagnosis, which provides references for researchers devoted to the related works. Finally, we discuss the challenges and prospects related to the work in this paper.

[1]  Bernhard Sick,et al.  The responsibility weighted Mahalanobis kernel for semi-supervised training of support vector machines for classification , 2015, Inf. Sci..

[2]  Slobodan Vucetic,et al.  Cold Start Approach for Data-Driven Fault Detection , 2013, IEEE Transactions on Industrial Informatics.

[3]  Bijaya K. Panigrahi,et al.  Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines , 2015, IEEE Transactions on Industrial Informatics.

[4]  Moamar Sayed Mouchaweh,et al.  Hybrid dynamic data-driven approach for drift-like fault detection in wind turbines , 2015, Evol. Syst..

[5]  Jingjing Xie,et al.  Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions , 2016 .

[6]  Enrico Zio,et al.  A data-driven approach for predicting failure scenarios in nuclear systems , 2010 .

[7]  Kuang-Chao Fan,et al.  A BPNN-PID based long-stroke nanopositioning control scheme driven by ultrasonic motor , 2012 .

[8]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[9]  Baijian Yang,et al.  Big Data Dimension Reduction Using PCA , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[10]  Zhongyang Fei,et al.  A novel SVM-RFE based biomedical data processing approach: Basic and beyond , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[11]  Kensuke Fukuda,et al.  ADMIRE: Anomaly detection method using entropy-based PCA with three-step sketches , 2013, Comput. Commun..

[12]  Yi-Qing Ni,et al.  Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN , 2016 .

[13]  Cheng Hua Li,et al.  Combination of modified BPNN algorithms and an efficient feature selection method for text categorization , 2009, Inf. Process. Manag..

[14]  Jianbin Qiu,et al.  Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults , 2017, Autom..

[15]  A BHAVANI SANKAR,et al.  Effective enhancement of classification of respiratory states using feed forward back propagation neural networks , 2013 .

[16]  Chengyou Cui,et al.  Real-time traffic signal learning control using BPNN based on predictions of the probabilistic distribution of standing vehicles , 2010, Artificial Life and Robotics.

[17]  Tetsuo Kirimoto,et al.  PCA-Based Detection Algorithm of Moving Target Buried in Clutter in Doppler Frequency Domain , 2011, IEICE Trans. Commun..

[18]  Jianbin Qiu,et al.  State Estimation in Nonlinear System Using Sequential Evolutionary Filter , 2016, IEEE Transactions on Industrial Electronics.

[19]  Jianbin Qiu,et al.  Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method , 2017, IEEE Transactions on Cybernetics.

[20]  Zhengkun Mi,et al.  Improved algorithms for high-dimensional robust PCA , 2016, 2016 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB).

[21]  Kate Smith-Miles,et al.  Kernal Width Selection for SVM Classification: A Meta-Learning Approach , 2005, Int. J. Data Warehous. Min..

[22]  Lei Liu,et al.  A multivariate statistical combination forecasting method for product quality evaluation , 2016, Inf. Sci..

[23]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .