# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 2 Related Works
### Fault Detection with One-Class Classification  
A number of previous studies have constructed fault detection models on the rotating machinery after feature extraction procedures [50; 51]. These studies have applied OCC methods to the extracted features because they only used the signal data obtained from the normal state of systems (these data are referred to as the target class in the OCC algorithms). Traditional OCC methods are based on the Hotelling's T\({}^{2}\) statistics, which summarize multiple features of the instantaneous operating state of rotating machinery. [PERSON] et al. [49] detected electric submersible pump (ESP) system faults through Hotelling's T\({}^{2}\) control chart. To this end, features of the signal are extracted by using PCA, and those extracted features are used to compute the Hotelling's T\({}^{2}\) statistics. In addition to Hotelling's T\({}^{2}\), several techniques, including support vector machines, tree structures, and \(k\)-NN structures, also have been incorporated into OCC methods. In recent years, [PERSON] et al. [52] used the support vector data description (SVDD), which is one of the most well-known OCC algorithms, to detect anomalous operating states in the rotating machinery. In addition, [PERSON] et al. [53] also used IsoForest trained with the manually defined features to detect faults in rolling bearing equipment. Even though these methods can create more sophisticated decision boundaries, to the best of our knowledge, they have been rarely applied to FDD problems. In recent years, DNN structures have been used on fault detection problems because they can simultaneously perform both the feature extraction and fault detection on the signal data through the end-to-end learning manner. Autoencoder (AE) is the most widely used DNN structure for fault detection problems with signal data [54; 55; 56]. In the fault detection problems, the AE is trained by minimizing the reconstruction error between the input target class and the reconstructed one. Because the AE structure is trained by only using the target class, the reconstruction error of the fault signal is much higher than that of the target class. Therefore, the reconstruction error can be used to quantify the amount of fault level. In this study, the approaches based on the reconstruction error of SAE or CAE are referred to as ReSAE or ReCAE, respectively. Anomaly detection with generative adversarial network (AnoGAN; [57]) and deep support vector data description (Deep SVDD; [58]) are the recently proposed DNN-based OCC algorithms. AnoGAN is based on a generative model [59], which consists of two individual neural networks referred to as the generator and the discriminator. The generator is trained to create fake data resembling the training samples as much as possible, whereas the discriminator is trained to identify whether given samples are real input or fake data created from the generator. Because these generators and discriminators are trained in an adversarial manner, the generator can create outputs more similar to the training samples. In the fault detection problems, AnoGAN is trained only using the target class, and it well produces the target class-like data. Conversely, inherent features of the fault signals cannot be reflected in AnoGAN, as fault signals cannot be appropriately generated. Therefore, for the fault signals, differences between generated ones and input signals are much larger than those of the target class. On the other hand, several DNN-based OCC algorithms adopting support vector-based approaches have also been proposed. The deep SVDD finds the smallest hypersphere enclosing most of the training target class within the latent features obtained from the DNN structures. In this algorithm, the latent features substitute the kernel function in the original SVDD algorithm. The latent features better discriminate against the fault signals than the conventional kernel function because they are derived by minimizing the loss function regarding fault detection problems.