Machine learning-based scheme for multi-class fault detection in turbine engine disks
Tài liệu tham khảo
Chen, 2020, Fault detection for turbine engine disk based on adaptive weighted one-class support vector machine, J. Electr. Comput. Eng., 2020, 1, 10.1155/2020/9898546
Dinesh Kumar, 1999, Evolutionary maintenance for aircraft engines
Abdul-Aziz, 2011, Rotor health monitoring combining spin tests and data-driven anomaly detection methods, Struct. Health Monit., 11, 3, 10.1177/1475921710395811
Hameed, 2009, Using FEM and CFD to locate cracks in compressor blades for non destructive inspections
Genest, 2018, Inspection of aircraft engine components using induction thermography
Zhao, 2010, Advancing feature selection research, 1
P. Langley, Selection of relevant features in machine learning, in: Proceedings of the AAAI Fall Symposium on Relevance, 1994, pp. 140–144.
Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach. Learn., 46, 389, 10.1023/A:1012487302797
C.E. Garcia, M.R. Camana, I. Koo, Real-time fault detection for turbine engine disk, in: International Conference on ICT Convergence (ICTC), Jeju, South Korea, 2020, pp. 1–6.
Freund, 1999, Large margin classification using the perceptron algorithm, Mach. Learn., 37, 277, 10.1023/A:1007662407062
Chuang, 2011, Improved binary particle swarm optimization using catfish effect for feature selection, Expert Syst. Appl., 38, 12699, 10.1016/j.eswa.2011.04.057
Mustapha, 2018, Multisource data fusion for classification of surface cracks in steel pipes, J. Nondestruct. Eval. Diagn. Progn. Eng. Syst., 1
Moreta, 2019, Prediction of digital terrestrial television coverage using machine learning regression, IEEE Trans. Broadcast., 65, 702, 10.1109/TBC.2019.2901409
Woike, 2013, New sensors and techniques for the structural health monitoring of propulsion systems, Sci. World J., 2013, 1, 10.1155/2013/596506
Abdul-Aziz, 2012, Turbine engine disk rotor health monitoring assessment using spin tests data, 248
Aziz, 2010, Propulsion health monitoring of a turbine engine disk using spin test data, 431
Chen, 2016, Fault detection for turbine engine disk based on an adaptive kernel principal component analysis algorithm, Proc IMechE, I: J. Syst. Control Eng., 230, 651, 10.1177/0959651816643670
Chen, 2017, Fault detection for turbine engine disk using adaptive Gaussian mixture model, Proc. Inst. Mech. Eng. I, 231, 827
Chen, 2020, Fault detection for turbine engine disk based on one-class large vector-angular region and margin, Math. Probl. Eng., 2020, 1
Jayasundara, 2020
Yoonus Nalakath1, 2014, Detection of a notch type damage using subspace identification and artificial neural networks, Int. J. Emerg. Technol. Adv. Eng., 4
Roy, 2020, Autocorrelation aided random forest classifier-based bearing fault detection framework, IEEE Sens. J., 20, 10792, 10.1109/JSEN.2020.2995109
Bandyopadhyay, 2019, Performance of a classifier based on time-domain features for incipient fault detection in inverter drives, IEEE Trans. Ind. Inf., 15, 3, 10.1109/TII.2018.2854885
Chen, 2007, Enhanced recursive feature elimination
Rotary dynamics laboratory at NASA Glenn research center, Disk defect data, Available in: https://c3.nasa.gov/dashlink/resources/314/.
Granitto, 2006, Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products, Chemometr. Intell. Lab. Syst., 83, 83, 10.1016/j.chemolab.2006.01.007
Darst, 2018, Using recursive feature elimination in random forest to account for correlated variables in high dimensional data, BMC Genet., 19, 65, 10.1186/s12863-018-0633-8
Bishop, 2006
Tharwat, 2016, Principal component analysis - a tutorial, Int. J. Appl. Pattern Recognit., 3, 197, 10.1504/IJAPR.2016.079733