# Machinery Fault Signal Detection with Deep One-Class Classification
## 2 Related Works
### Deep Neural Network-Based One-Class Classification Methods  
As mentioned earlier, for more successful fault signal detection, features specialized for the fault signal detection tasks should be extracted. To this end, deep neural network structures, which are designed to generate optimized features for given learning tasks, have been successfully used for fault signal detection in recent years [11; 12]. The deep neural network structure is composed of multiple intermediate layers (referred to as hidden layers) between input and output layers. These hidden layers generate features to minimize a loss function corresponding to the learning task without additional preprocessing or feature extraction procedures. Owing to this end-to-end learning manner, the deep neural network structure used for fault signal detection helps to achieve superior performance in that it can produce a specialized feature for anomalous signal detection tasks. The deep neural network has been usually used for supervised learning tasks with numerous labeled data. However, in many real situations, fault signals obtained from machinery system faults or malfunctions rarely exist compared to the signals obtained from normal operating status, and thus, label information on the machinery operating condition might not be available [13; 14]. Thus, these supervised deep neural networks cannot be used for fault signal detection tasks. For the unsupervised fault detection problem settings, unsupervised deep neural network-based OCC methods can be used. The unsupervised deep neural network-based OCC methods can be categorized as reconstruction-based [19; 20], generative adversarial network (GAN)-based [41], and boundary-based methods, according to the way to generate the features of input signals. Among them, the reconstruction-based OCC methods are based on autoencoder (AE)-based neural network structures. The AE-based neural network structures (e.g., stacked AE [11] and convolutional autoencoder (CAE [42; 43])) attempt to generate meaningful features by reconstruction of the input signal data in the output layer. In the AE neural network, the input signals are embedded into smaller dimensions of latent features (referred to as encoding), and the latent features reconstruct the input signals (referred to as decoding). For a more accurate reconstruction of input signals into the output layer, the latent features should retain the intrinsic properties of input signals as much as possible. The stacked AE neural network [11] and CAE neural network [42; 43] are the most widely used reconstruction neural network structures. These neural network structures can be used as OCC models by learning the neural network structures with only target signals (i.e., signals obtained from normal machinery status). Then, the errors between input signals and reconstructed outcomes can be used to calculate anomaly scores, quantifying the chances that the input signals are fault signals. That is to say; the target signals tend to have small reconstruction errors because the AE structures are constructed only using the training target signals. Conversely, the fault signals have larger reconstruction errors because the encoding structures are trained with no information on the fault signals, and the latent features cannot properly reconstruct the fault signals. In addition, the GAN-based OCC methods, which utilize neural network structures composed of both generator and discriminator, have recently been proposed. The generator attempts to create artificial signals as similar to input signals as possible, whereas the discriminator attempts to distinguish the artificial signals produced by the generator from the input signal as accurately as possible. Through an adversarial training scheme between the generator and discriminator, the GAN can produce artificial signals that have characteristics similar to target signals.