# Machinery Fault Signal Detection with Deep One-Class Classification
## 4 Experimental Study
### Experimental Settings
Figure 2: Test rig for the CWRU dataset (This figure 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)). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \
\begin{tabular}{c} **The Number of** \\ **Target Signals** \\ **(Normal Signals)** \\ \end{tabular} &
\begin{tabular}{c} **The Number of** \\ **All Fault Signals** \\ \end{tabular} \\ \hline Scenario 1 & 952 & 6108 \\ Scenario 2 & 1888 & 5160 \\ Scenario 3 & 1892 & 5172 \\ Scenario 4 & 1896 & 5180 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of target and fault signals in CWRU benchmark signal datasets. Figure 3: Example segmented signals of all bearing conditions in Scenario 1 of CWRU datasets. The Paderborn University dataset was collected by [PERSON] et al. (2018). This dataset is comprised of both vibration and current time series signals collected from the rolling bearing test rig shown in Figure 4.  
The vibration signals are collected by the accelerometer sensor installed at the top end of the rolling bearing module. These benchmark signals are collected from various operating scenarios by changing (1) rotating speed, (2) radial force onto the bearing, and (3) load torque in the drive train because vibration signals collected from different operating settings have different patterns. Table 3 shows the operating settings of four scenarios in the PU dataset. The dataset includes vibration signals from various types of bearing conditions: normal (Healthy), artificial inner race fault (AIR), artificial outer race fault (AOR), real inner race fault (RIR), and real outer race fault (ROR). In addition, the artificial fault types (AIR and AOR) are generated by electrical discharge machining (EDM), drilling, or electric engraver pitting, whereas real fault types (RIR and ROR) are made by accelerated life testing. Finally, among the six normal condition bearings having different operation times (bearing codes are K001, K002, K003, K004, K005, and K006), we employed the K003 bearing, which is operated for one hour. Hence, the normal signals obtained from K003 are used as target signals for OCC methods. The raw signals in the PU dataset were collected for approximately 4 s at a sampling rate of 64,000 Hz per second (i.e., each raw signal was recorded 256,000 times). Thus, similar to the CWRU dataset, each raw signal is sliced in order for individual signals to have 2048 time points (no overlap between signals) to comprise the datasets having sufficient signals to train the fault signal detection model. Then, the final fault signal detection model is constructed with the segmented signals. An example of these segmented signals to each bearing condition type is presented in Figure 5.  
As shown, the signals generated by the fault-bearing conditions have different patterns and scales compared to the signal obtained from normal conditions. In addition, fault signal detection datasets of four scenarios in PU benchmark signal datasets are summarized in Table 4.  
Figure 4: Test rig for the PU dataset. It consists of five modules: (1) electric motor, (2) torque-measurement shaft, (3) rolling bearing test module, (4) flywheel, and (5) load motor (2018). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rotating Speed** & **Radial Force** & **Load Torque** \\ \hline Scenario 1 & 1500 RPM & 1000 N & 0.7 Nm \\ Scenario 2 & 900 RPM & 1000 N & 0.7 Nm \\ Scenario 3 & 1500 RPM & 1000 N & 0.1 Nm \\ Scenario 4 & 1500 RPM & 400 N & 0.7 Nm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Operating scenarios in PU benchmark signal datasets.