## 3 Experimental Analysis  
### Data Analysis and Pre-Processing  
The dataset used for this research was the CWRU bearing dataset [48], one of the most popular bearing datasets, which is publicly provided by Case Western Reserve University on their website [2]. The CWRU bearing dataset is mostly used for fault analysis and classification and detection of faulty machinery bearings; thus, it serves as a fundamental dataset to authenticate the performance of the different ML- and DL-based algorithms. The bearing test rig arrangement used in obtaining the CWRU bearing data is shown in Figure 1. It consists of a 2 hp Reliance electric induction motor, a dynamometer, a torque transducer, and control electronics (not shown in the figure). Acceleration data are collected from many sensors placed at different locations. The data are collected for normal bearings, single-point drive-end (DE), and fan-end (FE) defects. This dataset consists of 161 records grouped into four classes: 48 k normal-baseline, 48 k drive-end fault, 12 k drive-end fault,and 12 k fan-end fault [10]. Electro-discharge machining was used to inject the single point faults into the test bearings, with fault diameters of 7 mils, 14 mils, 21 mils, 28 mils, and 40 mils. One mil is equal to 0.001 inches. Vibration data were recorded for motor loads of 0 to 3 horsepower, with motor speeds of 1720 to 1797 rpm after the faulty bearings were reinstalled into the test motor [48]. Regarding the names of the data files, the first letter represents the fault position, the next three numbers signify the fault diameters, and the last number denotes the bearing loads. For example, the data file 'B007_0' contains the ball bearing fault data, which has a fault of diameter 0.007 inches, operated under a motor load of 0 hp. Similarly, the data file 'OR014@6_1' contains the fault data of an outer-race fault of diameter 0.014 inches when the load was centered (fault in 6 o'clock position) and operated under a motor load of 1 hp [1,5].  
In this research, we used the 48 k DE fault data and 48 k normal-baseline data for the experiment. The fault types used were ball fault, inner-race fault, and outer-race fault for a motor load of 1 hp, 2 hp, and 3 hp for each type of fault. Each fault type was further categorized into the respective fault of 7 mils, 14 mils, and 21 mils. We also used a normal-baseline (healthy) bearing of 1 hp, 2 hp, and 3 hp load. The total number of datasets used was four and each set was divided into 10 classes. Datasets were named dataset A, dataset B, dataset C, and dataset D. Dataset A contained the 48 k DE data of load 1 hp, dataset B contained 48 k DE data of load 2, dataset C contained that of load 3 hp, and dataset D was the combination of all datasets A, B, and C. Table 1 shows the necessary details regarding the dataset used in this research.  
For the data pre-processing for the 1-D CNN, each signal from the 10 classes was further divided into \(N\) samples, each of 1600 points. Hence, the input for the 1-D CNN was 1600 \(\times\) 1. Figure 5 shows the sample division process for the 1-D CNN. Thus, the total number of samples used in model training was \(N\times 10\), which was further split at a train-test ratio of 0.7:0.3. Moreover, 10% of the train set was further used for the validation set. The value of N for dataset A, B, and C was 238, 303, and 303, respectively. Since dataset D was the combination of all datasets A, B, and C, it contained all the data from the earlier 3 datasets. Table 2 shows the length of the train, test, and validation sets for each dataset used in the research.