## 1 Introduction  
All objects on Earth are affected by the Earth's gravity. Conducting research on microgravity on the ground, instead of outer space, has many practical difficulties. On the other hand, research on hypergravity is relatively easy to carry out using the centrifugal force from a spinning simulation. Hypergravity research requires a gravity simulator that can control gravity by a constant rotation angular speed. Therefore, to conduct hypergravity research, a gravity simulator was developed to enable the formation and maintenance of a hypergravity environment of up to 15 times the Earth's gravity (15 G), as shown in Figure 1.  
Gravitational accelerators are generally used for the hypergravity training of astronauts and can be used for animal testing in basic research for medical purposes. In addition, they can be used to conduct experimental ground tests on the effects of sudden changes in gravity, such as hypergravity and hypogravity, and the changes in pressure that the human body undergoes in a space environment to investigate the biological responses to these harmful stimuli to the human body. These changes in gravity can result in fluid shiftsand redistribution in the human body, fluid loss, red blood cell loss, muscle damage, bone damage, hypercalcemia, immune system changes, or spatial disorientation and vertigo [1]. Many studies have examined the changes in the human and animal body due to changes in gravity. The necessity of monitoring the safety and reliability of large gravity acceleration equipment has become an important issue. One of the major issues regarding gravity acceleration equipment is the occurrence of abnormal vibrations when machinery failures occur due to high-speed rotation. The amplification of small vibrations generated in the rotating part of the gravity acceleration equipment may result in damage to the shafts rotating at high speeds, which may lead to serious accidents. Traditional machine learning (ML) that uses feature-based methods [3; 4; 5] on hand-crafted lists of feature engineering has limitations that cannot improve performance. Furthermore, it is difficult to say that human-designed features are for defective representation. The recently appeared Deep Neural Network (DNN) [6; 7; 8] has good performance, but it is difficult to describe the characteristics of the defect site due to the parameters of many hidden layers. This paper proposes a preventive maintenance model that enables the monitoring and visualizing of vibrations that can occur in machinery to proactively prevent the mechanical failures described above. The remainder of this paper is organized as follows. Section 2 briefly introduces the proposed method and dataset collected from the equipment used in the experiment. In Section 3, we experimented with various models and conditions to evaluate the performance of the proposed model. In addition, we also calculated fault scores through visualizations for each class of faults. Finally, Section 4 proposes a conclusion and future work. Related Work  
Fault Detection using ML: Many studies on vibration-related failures and predictive failure diagnosis have been conducted [3; 9; 10; 21]. [PERSON] et al. [3] proposed a rotating mechanism system--a mixture of feature extraction and selection classifies it as a Support Vector Machine (SVM) [4]. [PERSON] et al. [5] proposed fault detection for bearing wind turbines using ANNs (Artificial Neural Networks). [PERSON] et al. [13] adopted a method of learning via K-Nearest Neighbor (KNN), SVM, and Linear Discriminant Analysis (LDA) by screening features based on genetic algorithms to continuously check the state of ball bears in rotating ball bears of asynchronous electrical motors. [PERSON] et al. [14] proposed an algorithm  
Figure 1: Gravity Simulator for the research on hypergravity [2].