## 2 Related Work
### Anomaly Detection  
Anomaly detection refers to the process of recognizing deviations in a facility's operational patterns that significantly differ from normal conditions. This technique is crucial for the early identification and mitigation of potential faults [8]. Conventional methods for anomaly detection primarily rely on statistical analysis and signal processing. These techniques often involve setting thresholds using the mean and standard deviation or identifying anomalies through spectral analysis by examining frequency component variations. In recent years, machine learning and deep learning approaches have increasingly been applied to anomaly detection. Popular machine learning methods include Support Vector Machines (SVMs), Random Forests, and k-means clustering, whereas deep learning models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Autoencoders have been utilized for capturing complex anomaly patterns [29]. The strengths of these deep learning techniques are that they can effectively analyze large amounts of complex data and identify subtle anomaly patterns that are difficult to detect using traditional methods. Detecting anomalies in time series data necessitates understanding temporal dependencies across sequential observations. To achieve this, models like recurrent neural networks (RNNs), Gated Recurrent Units (GRUs), and transformer-based architectures are frequently employed [11]. These techniques are able to learn complex correlations in time series data and detect anomalies more accurately. In this paper, we utilize anomaly detection to identify defects in machinery in the manufacturing industry [30]. The classification of anomaly detection methods varies depending on the characteristics of the dataset. One common approach categorizes these methods based on whether labeled data samples are available. As illustrated in Table 1, anomaly detection techniques can be divided into three categories based on the utilization of abnormal data and the presence of labels. Supervised anomaly detection learns using normal and abnormal data with labels, and new data are automatically classified as normal or abnormal by analyzing them with a classifier. It is more accurate than other anomaly detection methods and is used in a wide range of fields, such as in Bayes classifiers and neural networks. However, this method depends on whether there are training data with initial label values. Because outliers are rare, it is generally difficult to obtain a sufficient number of labeled classes. Supervised anomaly detection requires data labeling, making it time-consuming and costly, and mistakes can occur during the labeling process. To compensate for these disadvantages, semi-supervised learning uses a small amount of labeled normal data in what is known as pseudo-labeling. It can save time and costs by not collecting abnormal data, but its accuracy is lower than that of supervised anomaly detection. Unsupervised anomaly detection uses unlabeled data and is widely used in industrial systems due to its ability to process a large amount of data. Principal component analysis and partial least squares are representative examples. However, this method is only effective for data with low correlation, and the data must follow a multivariate Gaussian distribution. Principal component analysis shows the poorest performance among the three anomaly detection methods. The anomaly detection techniques categorized by methodology are summarized in Table 2.  
In addition, anomalies, which are data points, bins, or structures that deviate from the normal pattern of the data, can be a sign of system failure, unusual activity, or an event of interest. In time series data, anomalies can be categorized into several types, each of which is defined and detected differently depending on the nature and context of the data. The main types of time series anomalies can be categorized into Global Anomaly, Contextual Anomaly, Shapelet Anomaly, Seasonal Anomaly, and Trend Anomaly [31].