## 2 Related Work  
In recent years, thanks to the advances in machine learning and computational technologies, anomaly detection algorithms have been widely applied in fraud detection, intrusion detection and fault detection in many different domains, including predictive maintenance in industrial processes [11; 12; 13; 14]. Predictive maintenance methods can be classified into three main categories: model-based approaches, case-(rule-)based approaches and data-driven approaches [15]. Over the years, data-driven predictive maintenance approaches have progressed and become crucial towards Industry 4.0 [16]. Recently, it has been shown that machine learning techniques increase the feasibility and availability of predictive maintenance, allowing its broad application including the fields that are previously considered hard to conduct maintenance [14; 17; 18]. Various machine learning approaches for clustering, classification and regression are widely applied for PM and proven to be effective [14; 18; 19; 20]. This includes different PM applications, such as the calculation of the Remaining Useful Life (RUL) [7; 21] or condition monitoring to prevent premature failures [3]. In this work, in order to exploit real-time information from massive data obtained from our conveyor belt and its peripheral devices, we apply an online condition monitoring that falls into the domain of anomaly detection. It refers to the problem of finding data points that do not follow the expected behavior. Such data points are usually named as anomalies, exceptions, outliers or novelties in different applications [22]. When a training dataset only involves \"normal conditions,\" the trained model should be capable of detecting whether a new observation belongs to the model. In that case, it is called a novel pattern that is then incorporated into the normal model after its detection. Any other previously unseen observation that is not categorized as novelty is then considered as an anomaly [23]. In our setup, since we have access mostly to the normal behavior data, we need a novelty detection algorithm that is also considered as a semi-supervised learning anomaly detection as it is a one-class classification problem [22]. Our goal is to classify new data as a novelty (part of normal behavior data) or as an anomaly (abnormal behavior data) if the test data does not relate to the training set [24]. There are various novelty detection techniques, which can be mainly categorized as distance-based, reconstruction-based, domain-based, and probabilistic methods. A widely used distance-based approach is \(k\)-nearest neighbor (\(k\)-NN) and is based on the assumption that normal data points all have close neighbors. Then, if a data point is far away from its neighbors, it is regarded as an anomaly [25]. An example of a domain-based novelty detection is one-class support vector machine (OCSVM). It is an unsupervised learning method that clusters only one class, which makes it suitable for a PM application detecting rare events with very small amount of abnormal behavior samples [26]. A decision boundary is calculated to indicate the area where most of data points are located. The area outside of this boundary is regarded as a novelty region. Kernel principle component analysis (PCA), on the other hand, is a reconstruction-based approach. It calculates a reconstruction error between the original and the reconstructed dataset, compares with a predefined threshold and decides the novelty of a new observation. Even though there are many other available approaches, they mostly suffer from problems such as excessive computational power requirements during the training stage if the dataset is large or not being sensitive to nonlinear data. Since a PM application is specific to data formats and patterns of different devices, we develop and compare most commonly applied methodologies that are suitable for our conveyor belt setup. For the purpose, we compare a \(k\)-NN, an OCSVM and a kernel PCA; and select the best performer to be adopted in our system.