# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Settings
For the reconstruction-based methods, we trained stacked AE and CAE and computed the reconstruction error,  
Figure 8: Example of acoustic signals obtained from our own rolling element experiment platform. \(\|x-\hat{x}\|^{2}\), between the input signal (\(x\)) and reconstructed outcome (\(\hat{x}\)) as an anomaly score. In this study, we refer to these reconstruction-based OCC methods as anomaly scores with reconstruction errors of the stacked AE (ReSAE) and the CAE (ReCAE), respectively. For the GAN-based method, we utilized an AnoGAN-based convolutional neural network structure to more effectively handle the signal-formatted data. We set the number of latent features of the AnoGAN model as 256, as suggested by [PERSON] et al. [44]. Finally, we applied the two deep SVDD models, soft-boundary deep SVDD and one-class deep SVDD, as boundary-based methods. In this study, we tried both stacked AE or CAE in the pre-training procedure of the deep SVDD model, in order to indicate the efficacy of convolutional blocks in order to analyze the signal data. In addition, to avoid the hyper-sphere collapse problem, we removed the bias term and used the leaky rectified linear unit activation function (Leaky ReLU). For the soft-boundary deep SVDD, we varied the hyperparameter \(\
u\) within the range of \(\
u\in\{0.01,0.02,0.03,\dots,0.1\}\), and selected one showed the best performance. The architecture details of the neural network structure for the deep SVDD are summarized in Table 6.  
In the current study, the neural network hierarchy structures, such as the number of convolutional and pooling layers and the sizes of filters, are specified the same as in the original deep SVDD literature [12]. As suggested in [12], these network structures have shown reasonable results in various cases. Accordingly, we also adopted the same network hierarchy structures as [12]. Please note that the bounded activation functions, including the sigmoid and hyperbolic tangent functions, tend to cause the hypersphere collapse problem [12]. Therefore, we employed the Leaky ReLU, which is the most well-known unbounded activation function. As for the deep SVDD, this method is implemented by using open source code released to the public (The deep SVDD by using open source code available at: [[https://github.com/lukasruff/Deep-SVDD-PyTorch](https://github.com/lukasruff/Deep-SVDD-PyTorch)]([https://github.com/lukasruff/Deep-SVDD-PyTorch](https://github.com/lukasruff/Deep-SVDD-PyTorch)) (accessed on 20 December 2023)). Finally, to conduct all experiments, we utilized an Intel(r) Core(TM) i5-9400F CPU @ 2.90 GHZ and NVIDIA GeForce RTX 2060 with 32 GB of RAM. All the DNN-based OCC methods were implemented with the GPU-accelerated Pytorch library (version 1.12.1) in Python (version 3.9.13). As mentioned earlier, the training dataset consists of only normal signals, and the testing dataset is composed of the signals both in normal operating conditions and all fault types. To this end, we composed the training dataset as randomly selected 80% of the normal signals, and the testing dataset is composed of both all fault signals and the remaining 20% of the normal signals. Then, we evaluate the fault signal detection performance of each method through the area under the receiver operating characteristic curve (AUROC) on the testing dataset. The ROC (receiver operating characteristic) curve represents the trade-off between two types of errors, true positive and false positive, in fault