1 |
S.M. Rezvanizaniani, J. Dempsey, and J. Lee, "An effective predictive maintenance approach based on historical maintenance data using a probabilistic risk assessment: PHM14 data challenge," Unpublished, 2014, [Online] Available: http://rgdoi.net/10.13140/RG.2.1.2076.2640.
|
2 |
C. Berenguer, M. Fouladirad, and E. Deloux, "Health-and-usage-based maintenance policies for a partially observable deteriorating system," Proceedings of the Institution of Mechanical Engineers, vol. 230, no. 1, pp. 120-129, 2015.
|
3 |
A. Chaudhuri, "Predictive Maintenance for Industrial IoT of Vehicle Fleets using Hierarchical Modified Fuzzy Support Vector Machine," Unpublished, 2018, [Online] Available: https://arxiv.org/abs/1806.09612.
|
4 |
Z. Wenjin, F. Mitra, and B. Berenguer, "A multi-level maintenance policy for a multi-component and multifailure mode system with two independent failure modes," Reliability Engineering & System Safety, vol. 153, pp. 50-63, 2016.
DOI
|
5 |
C. Prosper and D. West. "Case Study Applied Machine Learning to Optimise PCP Completion Design in a CBM Field," SPE Asia Pacific Oil and Gas Conference and Exhibition, vol. 10, no. 2018, 2018. DOI: 10.2118/192002-MS.
DOI
|
6 |
A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, and M. Figari, "Machine learning approaches for improving condition-based maintenance of naval propulsion plants." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 230, no. 1, pp. 136-153, 2014. DOI: 10.1177/1475090214540874.
DOI
|
7 |
I. A. Lawal and S. A. Abdulkarim, "Adaptive SVM for data stream classification," South African Computer Journal, vol. 29, no. 1, pp. 27-42, 2017. DOI: 10.18489/sacj.v29i.414.
|
8 |
G. J. J. Burg and P. J. F. Groenen, "GenSVM: A generalized multiclass support vector machine," The Journal of Machine Learning Research, vol. 17, no. 1, pp. 1-42, 2016.
|
9 |
K. A. Korba and F. Arbaoui, "SVM Multi-Classification of Induction Machine's bearings defects using Vibratory Analysis based on Empirical Mode Decomposition," International Journal of Applied Engineering Research, vol. 13, no. 9, pp. 6579-6586, 2018.
|
10 |
D. Chen, "Fault Classification Research of Analog Electronic Circuits Based on Support Vector Machine," Chemical Engineering Transactions, vol. 51, pp. 1333-1338, 2016.
|
11 |
Y. Prasad, K. K. Biswas, and P. Singla, "Feature selection using one class svm: A new perspective," MLCB, 2013, [online] Available: https://arxiv.org/abs/1508.07535.
|
12 |
A. Thomas, V. Feuillard, and A. Gramfort, "Calibration of One-Class SVM for MV set estimation," arXiv preprint arXiv:1508.07535, 2015, [Online] Available: https://arxiv.org/abs/1508.07535.
|
13 |
D. Droghini, D. Ferretti, E. Principi, S. Squartini, and F. Piazza., "A combined one-class SVM and template-matching approach for user-aided human fall detection by means of floor acoustic features," Computational Intelligence and Neuroscience 2017, 2017, [Online] Available:https://www.hindawi.com/journals/cin/2017/1512676/.
|
14 |
E. Burnaev and D. Smolyakov, "One-class SVM with privileged information and its application to malware detection," arXiv preprint arXiv:1609.08039, 2016, [Online] Available: https://arxiv.org/abs/1609.08039.
|
15 |
A. Saxenan, K. Goebel, D. Simon, and N. Eklund, "Damage propagation modeling for aircraft engine run-to-failure simulation," inProceeding of 2008 International Conference on Prognostics and Health Management, 2018. DOI: 10.1109/PHM.2008.4711414.
|