Browse > Article
http://dx.doi.org/10.11627/jkise.2020.43.1.123

An Ensemble Model for Machine Failure Prediction  

Cheon, Kang Min (Hyosung Information System)
Yang, Jaekyung (Dept. of Industrial and Information Systems Engineering, Jeonbuk National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.43, no.1, 2020 , pp. 123-131 More about this Journal
Abstract
There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.
Keywords
Ensemble Model; Anomaly Detection; STL and GESD; Clustering; Failure Prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Markou, M. and Singh, A., Novelty detection : a reviewpart 1: statistical approaches, Signal Processing, 2003, Vol. 83, No. 12, pp. 2481-2497.   DOI
2 Markou, M. and Singh, A., Novelty detection : a reviewpart 2 : neural network based approaches, Signal Processing, 2003, Vol. 83, No. 12, pp. 2499-2521.   DOI
3 Origins of the NIST/SEMATECH e-Handbook of Statistical Methods in the Work of Mary Natrella, http://www.itl.nist.gov/div898/handbook/, 2003.
4 Phoboo, A.E., Machine learning wins the Higgs challenge, 2014, No. BULNA-2014-265.
5 Punnoose, R. and Ajit, P., Prediction of employee turnover in organizations using machine learning algorithms, International Journal of Advanced Research in Artificial Intelligence, 2016, Vol. 5, No. 9, pp. 22-26.
6 Robert et al., STL : A Seasonal-Trend Decomposition Procedure Based on Loess, Journal of Official Statistics, 1990, Vol. 6, No. 1, pp. 3-73.
7 Liu, F.T., Ting, K.M., and Zhou, Z.-H., Isolation-based anomaly detection, ACM Transactions on Knowledge Discovery from Data, 2012, Vol. 6, No. 1, pp. 1-39.
8 Adam-Bourdarios et al., The Higgs boson machine learning challenge, NIPS 2014 Workshop on Highenergy Physics and Machine Learning, 2015.
9 Aggarwal, C., Outlier analysis, Springer, Switzerlnd, 2017.
10 Chandola, V., Banerjee, A., and Kumar, V., Anomaly detection : A survey, ACM Computing Surveys(CSUR), 2009, Vol. 41, No. 3.
11 In to the data, http://intothedata.com/02.scholar_category/anomaly_detection/.
12 Domingues, R., Filippone, M., Michiardi, P., and Zouaoui, J., A comparative evaluation of outlier detection algorithms : Experiments and analyses, Pattern Recognition, 2018, Vol. 74, pp. 406-421.   DOI
13 Fan et al., Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates : A case study in China, Energy Conversion and Management, 2018, Vol. 164, pp. 102-111.   DOI
14 Hadi, A.S., Identifying multiple outliers in multivariate data, Journal of the Royal Statistical Society : Series B (Methodological), 1992, Vol. 54, No. 3, pp. 761-771.   DOI
15 Jianliang, M., Haikun, S., and Ling, B., The application on intrusion detection based on k-Means cluster algorithm, 2009 International Forum on Information Technology and Applications, 2009, Vol. 1, pp. 150-152.
16 Joseph, M.P., A PD Validation Framework for Basel II Internal Ratings-Based Systems, Credit Scoring and Credit Control IV, 2005.
17 Knorr, E.M. and Ng, R.T., Finding intensional knowledge of distance-based outliers, in Proceedings of 25th International Conference on Very Large Databases, 1999.
18 Laskov, P., Dussel, P., Schafer, C., and Rieck, K., Learning intrusion detection : supervised or unsupervised?, In International Conference on Image Analysis and Processing, 2005, pp. 50-57.
19 Babajide Mustapha, I. and Saeed, F., Bioactive molecule prediction using extreme gradient boosting, Molecules, 2016, Vol. 21, No. 8, p. 983.   DOI