Ensemble fog computing architecture for unstable state detection of hydraulic system

Failure of machinery and equipment in factories, plants, etc. is a great loss to the operator. Various studies have been conducted for a long time to solve this problem, and many solutions have recently been developed to perform real-time health monitoring by attaching sensors to facilities. In addition, machine learning has allowed more accurate diagnosis of the condition to be carried out on these collected data. However, real-time fault detection is accompanied by a number of difficulties. Class imbalance issues are considered one of the most representative factors that makes it difficult to apply machine learning models in real-world locations. Class imbalance means that when it comes to classification problems, the amount of data in one class is greater than that in another class. In this case, the machine learning model is overfitted to a class with a lot of data, causing performance degradation. In real-world sites, the performance degradation of the model due to class imbalance is very serious because most data occurs in normal state, while fault data does not occur very well. Several prior studies have proposed ways to overcome this class imbalance, but each has several limitations. In this paper, an algorithm solution method and computing architecture solution method are introduced together as methodologies to overcome class imbalance, and a robust classification model is designed for class imbalance.