## 2 Related Works  
According to existing research, supervised learning techniques are used 88% of the time when analyzing equipment failure data, and unsupervised learning techniques 12% of the time [8]. When using supervised learning, a fault identification study is performedunder the assumption that data are collected with labels indicating the presence or absence of faults. When using unsupervised learning, research is conducted for anomaly detection. With unlabeled data, statistical methods such as regression analysis, time series analysis, and probabilistic models are studied to detect anomalies and construct a regression model to analyze abnormal conditions on ships and the changes detected [9]. When computer scientist [PERSON] began researching deep learning in 2010, it began replacing methods like regression models, support vector machines (SVM), and decision trees (DT), which were widely used as existing machine learning methods. The main reason for the rapid spread of deep learning is its high performance and ease of problem-solving by automating feature engineering (the most important step in machine learning). In machine learning, developers must create good features from the data, whereas deep learning simplifies the application of machine learning by learning all features at once. Research on monitoring and diagnosing equipment problems by utilizing these features of deep learning is underway. We will be examining related research on equipment abnormality detection using CNN after confirming the application cases of autoencoder, a deep learning method that is unsupervised [10; 11; 12]. We will be examining the latest research trends combining LSTMs with autoencoders, both of which are specialized for the analysis of time series data [13; 14; 15]. [PERSON] and [PERSON] [16] proposed a method of classifying data using the expectation-maximization technique and a Gaussian mixture model (GMM) and then monitoring performance using an autoencoder (AE, an unsupervised learning algorithm). The autoencoder is an unsupervised learning technique that uses neural networks to learn representations. When input comes in, the neural network compresses it as much as possible, extracts the characteristics of the data, and restores it to its original form. Anomaly detection is one of many applications of AE beyond its general use in dimension reduction. [PERSON] [17] applied a convolutional artificial neural network to diagnose the state of a rotating body in advance and used orbit analysis to diagnose the condition of the rotating body. The orbit represents the dynamics of the rotating body and visually expresses the state of the equipment. Additionally, the orbit shape is converted into an image, and the image pattern is used as input data for the convolutional artificial neural network. [PERSON] [18] studied failure diagnosis for journal-bearing rotor systems using a convolutional artificial neural network. They applied an omnidirectional regenerator (ODR) technique based on the vibration data acquired to obtain signals in the circumferential direction, used them to generate a vibration signal-based image and then used the image as input to a convolutional artificial neural network. [PERSON] et al. [19] conducted a study to diagnose roller bearing abnormalities using continuous wavelet transform and a convolutional artificial neural network. They propose image recognition based on time-frequency diagrams and Convolution Neural Networks (CNN). First, joint time-frequency analysis (JTFA) using continuous wavelet transform (CWT) of complex Morlet wavelet is used to obtain the time-frequency diagram characteristics of the vibration signal and normalize it to obtain the input of CNN. JTFA converts one-dimensional time domain signals into a two-dimensional representation of energy versus time and frequency. There are several different transforms available in the JTFA. A different time-frequency representation is shown for each transform type. Short-Time Fourier Transforms (STFT) are the simplest JTFA transformations.