Browse > Article
http://dx.doi.org/10.15207/JKCS.2022.13.03.023

A Comparative Study on the Methodology of Failure Detection of Reefer Containers Using PCA and Feature Importance  

Lee, Seunghyun (Dept. of MIS, Donga University)
Park, Sungho (Dept. of MIS, Donga University)
Lee, Seungjae (Dept. of MIS, Donga University)
Lee, Huiwon (Dept. of MIS, Donga University)
Yu, Sungyeol (Dept. of MIS, Catholic University of Pusan)
Lee, Kangbae (Dept. of MIS, Donga University)
Publication Information
Journal of the Korea Convergence Society / v.13, no.3, 2022 , pp. 23-31 More about this Journal
Abstract
This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.
Keywords
Refrigerated container; Fault detection; Machine learning; PCA; Feature selection; Feature importance;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 R. Yan, Z. Ma, Y. Zhao & G. Kokogiannakis. (2016). A decision tree based data-driven diagnostic strategy for air handling units. Energy and Buildings, 133, 37-45. DOI : 10.1016/j.enbuild.2016.09.039   DOI
2 Y. D. Yun, Y. W. Yang, H. S. Ji & H. S. Lim. (2017). Development of Smart Senior Classification Model based on Activity Profile Using Machine Learning Method. Journal of Cleaner Production, 8(1), 25-34. DOI : 10.15207/JKCS.2017.8.1.025   DOI
3 R. Tian, F. Chen & S. Dong. (2021). Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost. Mathematical Problems in Engineering, 2021. DOI : 10.1155/2021/2149048   DOI
4 M. J. Oh, E. S. Choi, K. W. Roh, J. S. Kim & W. S. Jo. (2021). A Study on the Design of Supervised and Unsupervised Learning Models for Fault and Anomaly Detection in Manufacturing Facilities. The Korean Journal of BigData, 6(1), 23-35. DOI : 10.5394/KINPR.2012.36.10.803   DOI
5 Z. Du, B. Fan, X. Jin & J. Chi. (2014). Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Building and Environment, 73, 1-11. DOI : 10.1016/j.buildenv.2013.11.021   DOI
6 K. B. Lee, S. H. Park, H. W. Lee, S. J. Lee & S. H. 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
7 R, Huang et al. (2018). An effective fault diagnosis method for centrifugal chillers using associative classification. Applied Thermal Engineering, 136, 633-642. DOI : 10.1016/j.applthermaleng.2018.03.041   DOI
8 A. Priyam, G. R. Abhijeeta, A. Rathee & S. Srivastava. (2013). Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology, 3(2), 334-337.
9 D. A. T. Tran, Y. Chen, H. L. Ao & H. N. T. Cam. (2016). An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts. International Journal of Refrigeration, 72, 81-96. DOI : 10.1016/j.ijrefrig.2016.07.024   DOI
10 G. LI et al. (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
11 G. Chandrashekar& F. Sahin. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. DOI : 10.1016/j.compeleceng.2013.11.024   DOI
12 H. J. Han, D. K. Ko & H. C. Choe. (2019). Prediction and Analyzing Factor Affection Financial Stress of Household Using Machine Learning: Application of XGBoost. Journal of Consumer Studies, 30(2), 21-43. DOI : 10.35736/JCS.30.2.2   DOI
13 D. Li, G. Hu & C. J. Spanos. (2016). A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis. Energy and Buildings, 128, 519-529. DOI : 10.1016/j.enbuild.2016.07.014   DOI
14 T. Chen & C. Guestrin. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-794. DOI : 10.1145/2939672.2939785   DOI
15 B. Castelein, H. Geerlings & R. Van Duin. (2020). The reefer container market and academic research: A review study. Journal of Cleaner Production, 256, 120654. DOI : 10.1016/j.jclepro.2020.120654   DOI
16 A. Kan, T. Wang, D. 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
17 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
18 G. S. Gim, H. S. Shon, K. H. Ryu & S. H. Lee. (2013). Performance of PCA Algorithm for Multivariate Data Analysis. In Proceedings of the Korea Information Processing Society Conference (pp. 1264-1266). Korea Information Processing Society. DOI : 10.3745/PKIPS.Y2017M11A.1264   DOI
19 N. Hoffmann, R. Stahlbock & S. Voss. (2020). A decision model on the repair and maintenance of shipping containers. Journal of Shipping and Trade, 5(1), 1-21. DOI : 10.1186/s41072-020-00070-2   DOI
20 S. Cateni, M. Vannucci, M. Vannocci & V. Colla. (2012). Variable selection and feature extraction through artificial intelligence techniques. Multivariate Analysis in Management, Engineering and the Science, 103-118. DOI : 10.5772/53862   DOI
21 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(11), 15-21. DOI : 10.15207/JKCS.2019.10.11.015   DOI