# Machinery Fault Signal Detection with Deep One-Class Classification
## 2 Related Works
### Deep Neural Network-Based One-Class Classification Methods
Anomaly generative adversarial network (AnoGAN [44]), which is the most well-known GAN-based OCC method, is trained only using target signals, and thus, the generator produces a signal having similar patterns of normal operating status. Therefore, the generator can only generate an artificial signal similar to the training target signals, and the anomaly score of the newly collected signal is defined as the difference between the new signal and the generated signal from the generator of the AnoGAN. Finally, a signal having a large anomaly score is rejected as a fault signal. In addition to these methods, more recently, deep neural network structures have been successfully used for unsupervised anomaly detection problems in various unstructured datasets. For instance, [PERSON] et al. proposed a sparse recurrent neural network (sRNN [45]) method to detect anomalous patterns in video datasets. Furthermore, self-supervised learning-based OCC methods have also been used for anomaly detection problems in image-formatted datasets [46; 47]. Although these existing deep neural network-based OCC methods render reasonable results within the situations for which they were designed, no consensus exists regarding the best all-around performer in real situations. Firstly, reconstruction-based methods are designed to reproduce the original signal data simply by minimizing reconstruction errors. However, the reconstruction of the input signal might not be directly associated with the classification between target and fault signals [20; 48]. Therefore, the reconstruction errors cannot be properly used as anomaly scores. Moreover, the GAN-based methods often generate inappropriate artificial signals because they often suffer from mode collapse problems [49]. The mode collapse problem is that the generator part tends to be trained to create only artificial signals with patterns that are almost similar to the most representative training target signal in order to minimize the discriminator's loss. The mode collapse problem causes a lack of diversity of generated signals, and it eventually yields poor fault signal detection performance of the GAN-based OCC methods. Finally, the most recently proposed deep neural network-based OCC methods are designed for specific domains, such as anomaly video detection (sRNN) or unusual image detection (self-supervised approach-based methods). Therefore, these methods might not be properly utilized for fault signal detection problems. To overcome the above limitations, we propose to use deep support vector data description (deep SVDD), the most well-known boundary-based method, for fault signal detection problems. The deep SVDD method has shown superior anomaly detection performance in various cases, only using the target class. Please note that [PERSON] et al. [50] have extended the deep SVDD methods as deep semi-supervised anomaly detection (deep SAD) methods, which use a limited number of anomaly samples. This deep neural network-based OCC method encourages the anomaly samples to be located outside the hypersphere as much as possible. By doing so, the decision boundary can be improved to discriminate between target class and anomaly. However, this method cannot be applied to the problem setting considered in this study, in which only the target class (i.e., time series signals collected from normal status) is available in the training phase. Therefore, we propose to use the deep SVDD as a fault signal detection method.