## 3 Experimental Setup  
### Slew Bearing Test-Rig and Data Acquisition  
The run-to-failure data used in this paper was collected from a slew bearing test rig. The test rig was designed to replicate an actual condition in steel mill manufacturing that operates the bearing in a low rotational speed, high load, and dust environment. Figure 1 shows the schematic of the slew bearing test rig, including the main drive gear reducer, the hydraulic load, and how the bearing is attached. A detailed sensors placement is presented in Figure 2. Four accelerometers, two AE sensors, and four temperature sensors were used during the experiment. Two accelerometers of IMI 608A11 ICP type sensors with a sensitivity of 100 mV/g and frequency range of 0.5 to 10 kHz, and two accelerometers of IMI 626B02 ICP type sensors with a sensitivity of 500 mV/g and frequency range of 0.2 to 6 kHz, were used. The IMI 608A11 ICP type sensors were installed on the inner radial surface at 180 degrees to each other and the IMI 626B02 ICP type sensors were attached on the axial surface at 180 degrees to each other. Similar to the measurement in continuous rotation, these accelerometers were connected to a high speed Pico scope DAQ (PS3424). The IMI 626B02 ICP type accelerometers were selected because of the minimum frequency range of 0.2 Hz and because the sensitivity is higher than that of the IMI 626B02 ICP type accelerometer. The vibration signal was acquired using a 4880 Hz sampling rate. A three-axes (two axial rows and one radial row) brand new slew bearing is used in this experiment. Each axial and radial row has dozens of rollers inside. The slew bearing is typically large in dimension and is usually used to support a high axial and radial load [50]. The bearing attachment \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \multicolumn{2}{c}{**Classification**} & \multicolumn{2}{c}{**Method or Algorithm**} & **Features** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Model-based approaches:} & - & Paris’ formula [24; 25] & N/A \\  & - & Physics-based methods & - & Stiffness-based model [26] & N/A \\  & - & State space-based methods & - & Kalman filter [27] & N/A \\  & & - & Particle filter [28] & RMS and envelope \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{Reliability-based method:} & - & Gaussian process models [29] & \multirow{4}{*}{Rényi entropy} \\  & & - & PIM [30] & & Kurtosis \\  & & - & PCM 1 [31] & & Principal features \\  & & - & Stochastic model [32] & N/A \\  & & probability models: & - & PHM [33; 34] & N/A \\  & & - & Weibull distribution [35] & N/A \\  & & - & HMM [36] & RMS \\  & & - & WPD 2 and HMM [37] & Peak-to-peak, energy and kurtosis \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Data-driven approaches:} & - & ANN [38; 39] & \multirow{4}{*}{N/A} \\  & & - & Fuzzy logic [40] & & \\  & & - & Genetic algorithm [41] & N/A \\ \cline{1-1}  & - & AI methods & - & ALE 3  & \multirow{4}{*}{Monitoring index} \\  & - & Regression methods & - & Recursive least square [25] & RMS \\ \cline{1-1}  & - & AI + regression methods & - & Dempster-Shafer regression [43] & RMS and envelope \\ \cline{1-1}  & - & AI & ARMA/GARCH 4 model [44] & RMS and envelope \\ \cline{1-1}  & & - & RVM 5 and LR 6 [45] & Kurtosis \\ \cline{1-1}  & & - & RVM and exp. regression [46] & RMS \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Data-driven + reliability-based methods} & - & CPH 7 model + SVM 8 [33] & \multirow{4}{*}{Kurtosis} \\  & & - & (ARMA, PHM) + SVM [34] & Peak value and RMS \\ \cline{1-1}  & & - & SVM + survival probability [47] & Kurtosis \\ \cline{1-1}  & & - & RVM + survival probability [48] & RMS, kurtosis and entropy \\ \cline{1-1}  & & - & ANN + Weibull distribution [49] & estimation \\ \hline \end{tabular}  
* PCM: Proportional Covariates Model. 2 WPD: Wavelet Package Decomposition. 3 ALE: Adaptive Line Enhancer.
* GARCH: Generalized Autoregressive Conditional Heteroskedasticity. 5 RVM: Relevance Vector Machine. 6 LR: Logistic Regression. 7 CPH: Cox-Proportional Hazard. 8 SVM: Support Vector Machine.  
\end{table}