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http://dx.doi.org/10.15207/JKCS.2022.13.01.023

Development of machine learning model for reefer container failure determination and cause analysis with unbalanced data  

Lee, Huiwon (Dept. of MIS, Donga University)
Park, Sungho (Dept. of MIS, Donga University)
Lee, Seunghyun (Dept. of MIS, Donga University)
Lee, Seungjae (Dept. of MIS, Donga University)
Lee, Kangbae (Dept. of MIS, Donga University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.1, 2022 , pp. 23-30 More about this Journal
Abstract
The failure of the reefer container causes a great loss of cost, but the current reefer container alarm system is inefficient. Existing studies using simulation data of refrigeration systems exist, but studies using actual operation data of refrigeration containers are lacking. Therefore, this study classified the causes of failure using actual refrigerated container operation data. Data imbalance occurred in the actual data, and the data imbalance problem was solved by comparing the logistic regression analysis with ENN-SMOTE and class weight with the 2-stage algorithm developed in this study. The 2-stage algorithm uses XGboost, LGBoost, and DNN to classify faults and normalities in the first step, and to classify the causes of faults in the second step. The model using LGBoost in the 2-stage algorithm was the best with 99.16% accuracy. This study proposes a final model using a two-stage algorithm to solve data imbalance, which is thought to be applicable to other industries.
Keywords
Reefer Container; Machine Learning; DNN; Fault Diagnosis; Cause analysis; Multi-class; Fault type classification;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 A. More. (2016). Survey of resampling techniques for improving classification performance in unbalanced datasets. arXiv preprint arXiv:1608.06048
2 Y. Wang, Z. Wang, S. He & Z. Wang. (2019). A practical chiller fault diagnosis method based on discrete Bayesian network. International Journal of Refrigeration, 102, 159-167. DOI : 10.1016/j.ijrefrig.2019.03.008   DOI
3 Y. D. Yun, Y. Y. Yang, H. S. Ji & H. S. Lim, (2017) Development of Smart Senior Classification Model based on Activity Profile Using Machine Learning Method. Journal of the Korea Convergence Society, 8(1), 25-34, DOI : 10.15207/JKCS.2017.8.1.025   DOI
4 B. Jin, D. Li, S. Srinivasan, S. K. Ng, K. Poolla & A. Sangiovanni-Vincentelli. (2019). Detecting and diagnosing incipient building faults using uncertainty information from deep neural networks. In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). 1-8. DOI : 10.1109/ICPHM.2019.8819438   DOI
5 J. Loisel, S. Duret, A. Cornuejols, D. Cagnon, M. Tardet, E. Derens-Bertheau & O. Laguerre. (2021). Cold chain break detection and analysis: Can machine learning help?. Trends in Food Science & Technology, 112, 391-399. DOI : 10.1016/j.tifs.2021.03.052   DOI
6 Y. Fan, X. Cui, H. Han & H. Lu. (2020). Feasibility and improvement of fault detection and diagnosis based on factory-installed sensors for chillers. Applied Thermal Engineering, 164, 114506. DOI : 10.1016/j.applthermaleng.2019.114506   DOI
7 G. Li, Y. Hu, H. Chen, L. Shen, H. Li, M. Hu & K. Sun. (2016). An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm. Energy and Buildings, 116, 104-113. DOI : 10.1016/j.enbuild.2015.12.045   DOI
8 X. Liu, Y. Li, X. Liu & J. Shen. Fault diagnosis of chillers using very deep convolutional network. In 2018 Chinese Automation Congress (CAC) . IEEE. 1274-1279. DOI : 10.1109/CAC.2018.8623749   DOI
9 S. K. Park, Y. G. Park & Y. R. Shin. (2012). A Study on the Improvement of Damage to Reefer Container Cargo. Journal of Navigation and Port Research, 36(10), 803-810. DOI : 10.5394/KINPR.2012.36.10.803   DOI
10 S. B. Yang (2018). A Study on the Monitering Systems of Reefer Containers. thesis dissertation. KOREA MARITIME & OCEAN UNIVERSITY. Busan.http://kmou.dcollection.net/common/orgView/200000016889
11 H. J. Park. (2020). Trend Analysis of Korea Papers in the Fields of 'Artificial Intelligence', 'Machine Learning' and 'Deep Learning'. Korea Information Electron Communication Technology, 13(4), 283-292. DOI : 10.17661/jkiiect.2020.13.4.283   DOI
12 P. Tang, O. A. Postolache, Y. Hao & M. Zhong. (2019). Reefer Container Monitoring System. In 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE) (pp. 1-6). IEEE. DOI : 10.1109/ATEE.2019.8724950   DOI
13 A. Kan, T. Wang, W. Zhu & D. Cao. (2021). The characteristics of cargo temperature rising in reefer container under refrigeration-failure condition. International Journal of Refrigeration, 123, 1-8. DOI : 10.1016/j.ijrefrig.2020.12.007   DOI
14 K. B. Lee, S. H. Park, S. H. Sung & D. M. Park. (2019). A Study on the Prediction of CNC Tool Wear Using Machine Learning Technique. Journal of the Korea Convergence Society, 10(10), 15-21. DOI : 10.15207/JKCS.2019.10.11.015   DOI
15 G. Li, Q. Yao, C. Fan, C. Zhou, G. Wu, Z. Zhou & X. Fang. (2021). An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems. Building and Environment, 108057. DOI : 10.1016/j.buildenv.2021.108057   DOI
16 K. B. Lee, S. H. Park, H. W. Lee, S. H. Lee & S. J. Lee, (2021) A study on the 3-step classification algorithm for the diagnosis and classification of refrigeration system failures and their types. Journal of the Korea Convergence Society, 12(8), 31-37. DOI : 10.15207/JKCS.2021.12.8.031   DOI