# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study  
### Experimental Settings  
To demonstrate the superiority of the deep SVDD model for the fault signal detection problems, we performed an experimental study with signal datasets in three cases. First, we utilized the two datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU), which are the most widely used benchmark fault signals used in a number of previous studies. In addition to these two benchmark datasets, we collected both vibration and acoustic signal datasets collected from our own rolling element experiment platform. In this study, we used the time series signals collected from rolling element bearing because the it is the most representative rotating machinery. Hence, through this experimental study, we demonstrated the applicability of the deep SVDD in real world machinery fault detection problems. More detailed description of each signal datasets are presented as follows:  
Case 1: Case Western Reserve University (CWRU) dataset--Vibration signal (The CWRU benchmark dataset is available at: [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)) (accessed on: 20 December 2023)). The CWRU bearing dataset was collected from the accelerometer sensor attached to the bearing installed in fan end side of the test rig, as depicted in Figure 2.  
In this study, we used the signal datasets collected by the accelerometer sensor attached in the fan end side. These benchmark signals are collected from various operating scenarios by changing the rotating speeds and vertical loads because vibration signals collected from different operating settings have different patterns. Table 1 shows the operating settings of four scenarios in the CWRU dataset. The raw signals in this benchmark dataset were recorded at a sampling rate of 12,000 Hz per second (i.e., each raw signal was recorded as 12,000 time points per second). In addition, the dataset includes vibration signals from four types of bearing conditions: normal, inner race fault, outer race fault, and ball element fault. In these benchmark datasets, each fault type has four intensities corresponding fault diameters (e.g., 7 inch (weak fault), 14 inch (medium fault), 21 inch (strong fault)). Therefore, the CWRU dataset has ten bearing condition types, and signals from the normal condition are considered as target signals for OCC methods. In this benchmark dataset, the ten vibration signals corresponding to each condition type were collected in approximately 10 to 20 s. In order to comprise the datasets having a sufficient number of signals to train the fault signal detection model, we segmented each raw signal into smaller ones. To this end, each raw signal is sliced in order for individual signals to have 1024 time points, and the sliced signals are overlapped each other with 768 time points. Then, the final fault signal detection model is constructed with the segmented signals. An example of these segmented signals to each bearing type is presented in Figure 3. As shown, the signals generated by the fault-bearing conditions have different patterns compared to the signals obtained from normal conditions. In addition, fault signal detection datasets of four scenarios in CWRU benchmark signal datasets are summarized in Table 2. \begin{table}
\begin{tabular}{c c c} \hline  & **Rotating Speed** & **Vertical Load** \\ \hline Scenario 1 & 1797 RPM & 0 HP (No pressure) \\ Scenario 2 & 1772 RPM & 1 HP \\ Scenario 3 & 1750 RPM & 2 HP \\ Scenario 4 & 1730 RPM & 3 HP \\ \hline \end{tabular}
\end{table} 
Table 1: Operating scenarios in CWRU benchmark signal datasets.