A study on Data Preprocessing for Developing Remaining Useful Life Predictions based on Stochastic Degradation Models Using Air Craft Engine Data |
Yoon, Yeon Ah
(Department of Industrial and Management, Kyonggi University Graduate School)
Jung, Jin Hyeong (Department of Industrial and Management, Kyonggi University Graduate School) Lim, Jun Hyoung (Intelligent System Engineering Division, Hancom MDS) Chang, Tai-Woo (Department of Industrial and Management, Kyonggi University) Kim, Yong Soo (Department of Industrial and Management, Kyonggi University) |
1 | Ann, G., Yoo, J.H., Lee, S.H., and Kim, S.B., Explainable convolution neural networks for multi-sensor data, Journal of the Korean Institute of Industrial Engineers, 2019, Vol. 45, No. 2, pp. 146-153. DOI |
2 | Baik, J., AI techniques for prognostics and health management, Journal of Applied Reliability, 2019, Vol. 19, No. 3, pp. 243-255. DOI |
3 | Bontempi, G., Taieb, S.B. and Le Borgne, Y.A., Machine learning strategies for time series forecasting, European Business Intelligence Summer School, Springer, Berlin, Heidelberg, 2012, pp. 62-77. |
4 | Chang, J.-H., A fuzzy window mechanism for information differentiation in mining data streams, Journal of the Korea Academia-Industrial Cooperation Society, 2011, Vol. 12, No. 9, pp. 4183-4191. DOI |
5 | Chen, T. and Guestrin, C., Xgboost : A scalable tree boosting system, Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Datamining, 2016, pp. 785-794. |
6 | Chiu, S.C., Li, H.F., Huang, J.L., and You, H.H., Incremental mining of closed inter-transaction itemsets over data stream sliding window, Journal of Information Science, 2011, Vol. 37, No. 2, pp. 208-220. DOI |
7 | Choi, W., Chang, S., Lee, S., Kang, H., Bang, M., and Bae, Y., Development of degradation evaluation SW for high temperature component using machine learning approach, The Korean Society of Mechanical Engineers, 2020, Vol. 44, No. 1, pp. 57-62. DOI |
8 | Coble, J.B. and Hines, J.W., Prognostic algorithm categorization with PHM challenge application, 2008 International Conference on Prognostics and Health Management, IEEE, 2008, pp. 1-6. |
9 | Friedman, J.H., Greedy function approximation : a gradient boosting machine, Annals of Statistics, 2001, pp. 1189-1232. |
10 | Heimes, F.O., Recurrent Neural Networks for Remaining Useful Life Estimation, 2008 International Conference on Prognostics and Health Management, IEEE, 2008, pp. 1-6. |
11 | Breiman, L., Random forests, Machine Learning, 2001, Vol. 45, No. 1, pp. 5-32. |
12 | Jung, H. and Kim, J.-W., A machine learning approach for mechanical motor fault diagnosis, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vo1. 40, No. 1, pp. 57-64. DOI |
13 | Seo, S., Kang, J., Nam, K.W., and Ryu, K.H., A sliding window-based multivariate stream data classification, The Korean Institute of Information Scientists and Engineers, 2006, Vol. 33, No. 2, pp. 163-174. |
14 | Koc, C.K., Analysis of sliding window techniques for exponentiation, Computers and Mathematics with Applications, 1995, Vol. 30, No. 10, pp. 17-24. DOI |
15 | Laguna, J.O., Olaya, A.G., and Borrajo., D., A dynamic sliding window approach for activity recognition, International Conference on User Modeling, Adaptation, and Personalization, pringer, Berlin, Heidelberg, 2011, pp. 219-230. |
16 | Le Son, K., Fouladirad, M., Barros, A., Levart, E., and Lung, B., Remaining useful life estimation based on stochastic deterioration models : A comparative study, Reliability Engineering and System Safety, 2013, Vol. 112, pp. 165-175. DOI |
17 | Lee, H.S. and Oh, S.H., A Study on the development of time series forecasting model for corporate credit risk using machine learning, The Koran Society of Management information Systems, Seoul, Korea, 2019, pp. 396-405. |
18 | Peel, L., Data driven prognostics using a kalman filter ensemble of neural network models, 2008 International Conference on Prognostics And Health Management, IEEE, 2008, pp. 1-6. |
19 | Sim, H.S., Kang, J.-G., and Kim, Y.S., A review on prognostics and health management : 2013-2018, Journal of Applied Reliability, 2019, Vol. 19, No. 1, pp. 68-84. DOI |
20 | Song, H.S., Seo, Y.K., Jung, D.H., and Park, B.H., A case study of degradation analysis for the passenger vehicles shock absorber, Journal of Applied Reliability, 2017, Vol. 17, No. 3, pp. 181-187. |
21 | Wang, T., Yu, J., Siegel, D., and Lee, J., A similaritybased prognostics approach for remaining useful life estimation of engineered systems, 2008 International Conference on Prognostics and Health Management, IEEE, 2008, pp. 1-6. |