| Title |
Developing Operation Fault Detection for Freezer : A Comparative Study of Machine Learning Algorithms |
| Authors |
Bok Han Kim ; Seung Yeon Choi ; Sean Hay Kim |
| DOI |
https://doi.org/10.6110/KJACR.2018.30.5.237 |
| Keywords |
기계학습 ; 냉동고 ; 운영 오류 ; 진단 ; 예측 Machine learning ; Freezer ; Operation fault ; Diagnosis ; Prediction |
| Abstract |
This study aims to diagnose operation faults of freezer such as door left open by mistakes and refrigerant leaks by using machine learning approach. Machine learning algorithms can take training raw data and then output trained model that contains prediction rules. Active power of freezer, laboratory ambient temperature, and freezer inside surface temperature are selected as monitoring variables. Heat capacity, refrigerant mass, and door opening also varied upon actual operation scenarios. About 190,000 raw data were collected. We selected five machine learning algorithms: SVM, DT, KNN, ANN, and Naive Bayesian Classification. Kernel-based classification algorithms such as KNN and SVM were found to have better performance in diagnosing operation faults of freezer than other machine learning algorithms. |