Application of various machine learning models for fault detection in the refrigeration system of a brewing company

Abstract

Industrial refrigeration systems typically exhibit a higher energy consumption rate compared to other systems. It is a very important system, and for the brewing industry, it is at the heart of brewing as, in each process, the temperature of the beer must be controlled. Therefore, a health monitoring or faults detection system is a necessary tool for predictive and preventive maintenance to avoid faults in the system. This work presents machine learning models for faults prediction of the refrigeration system in a brewing firm. The raw data of processing parameters are collected, while the system health for each data set is identified and used for machine learning model training. Three operation cases based on the actual operation of the brewery's refrigeration system are analyzed in this work. Several machine learning models including Naïve Bayes (NB), Generalized Linear Models (GLMs), Logistic Regression (LR), Fast Large Margin (FLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), and Support Vector Machine (SVM) are applied for refrigeration system fault detection in the three operation cases while their performances are investigated. The numerical simulation is performed based on RapidMiner Studio. According to the experiment of the three operation cases, the deep learning model was found to be the most accurate and required the least amount of time for the analysis. The accuracy percentages are 86.7%, 90.2%, and 82.5%, while the running times are 15 seconds, 27 seconds, and 15 seconds, respectively. This work can be considered as the baseline for future studies on applied machine learning models for fault detection in refrigeration systems.