# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
## 4 Experimental Study  
### Experimental Data Presentation  
With the aim of evaluating the effectiveness of the MBCNN-BiLSTM model introduced in this paper in RUL prediction, the accelerated degradation dataset of rolling bearings published in the IEEE 2012 PHM Data Challenge [27] in 2012 was used. This dataset was collected on the PRONOSTIA bearing accelerated degradation experimental platform, which is arranged as shown in Figure 7.  
Figure 7: PRONOSTIA bearing accelerated degradation experimental platform.  
The wear is the main form of failure of rolling bearings, which generally occurs between the raceway and the rolling element. The wear will make the bearing clearance and surface roughness increase, and the rotational accuracy decrease, and will also intensify the bearing wear, which is mainly manifested as a vibration amplitude increase in vibration, so the fault diagnosis technology based on vibration signal has been widely used. Compared with signals such as temperature images, current and voltage signals, sound signals, and oil analysis, vibration signals respond quickly to changes in the state of the bearing, and contain the most useful information about changes in the operating state of the bearing; they are also easy to extract. Therefore, in this paper, the RUL of bearings is predicted based on vibration signals.  
The experimental data were obtained by acquiring both horizontal and vertical acceleration sensors mounted on the bearing housing. The acquisition interval of the acceleration sensor is 10 s, the acquisition frequency is 25.6 kHz, and the time of each acquisition is 0.1 s, i.e., the number of data points acquired each time is 2560. If the vibration amplitude of the bearing vibration signal is more than 20 g, it is considered that the bearing is no longer usable. Its purpose is to prevent the destruction of the platform in the experiment while the amplitude of the bearing vibration signal is too large. The equipment collected vibration signals in both horizontal and vertical directions. It was studied that the horizontal signal provides more effective information than the vertical signal [28; 29]; therefore, the horizontal signal was chosen for the experiments in this paper. The sampling schematic of this dataset is illustrated in Figure 8.  
Figure 9 shows the vibration waveforms in the horizontal direction of some bearings in operating condition 1. From the figure, it can be seen that as the bearings continue to degrade, their vibration amplitude becomes larger and larger, eventually leading to failure.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Conditions 1** & **Conditions 2** & **Conditions 3** \\ \hline Radial force/N & 4000 & 4200 & 5000 \\ Rotational Speed/r\(\cdot\)min\({}^{-1}\) & 1800 & 1650 & 1500 \\  & B1\_1 & B2\_1 & B3\_1 \\  & B1\_2 & B2\_2 & B3\_2 \\  & B1\_3 & B2\_3 & B3\_3 \\ Bearing dataset & B1\_4 & B2\_4 & - \\  & B1\_5 & B2\_5 & - \\  & B1\_6 & B2\_6 & - \\  & B1\_7 & B2\_7 & - \\ \hline \end{tabular}
\end{table}
Table 1: Description of the bearing dataset.  
Figure 8: Sampling schematic of bearing vibration signal.