## 2 Dataset Description and Processing  
The test rig depicted in Figure 1 was utilized to produce the data used in this study. It consists of a high-speed spindle that rotates a shaft supported by three roller bearings, two accelerometers, and a load cell. The load cell is used to measure the axial force applied to the shaft, while the accelerometers are positioned at two separate points along the shaft, A1 and A2. Additionally, the test rig has a lubrication system distributing oil to the bearingsthroughout the test. The test rig was developed to monitor system vibration while different measurements are taken under varying operating conditions with variously damaged bearings. Specially designed for high-speed aerospace applications, bearings designated B1, B2, and B3 have varying characteristics in terms of pitch diameter, roller diameter, contact angle, and number of rolling components. In fact, the introductory paper provides a comprehensive analysis of the behavior of different bearing types and sizes under various working conditions. However, it is worth mentioning that the authors did not study each bearing separately. Instead, these bearings were studied as a single system coupled to a single shaft. This is the reason why the authors carried out all their experiments on the B1 bearing (see first paragraph of Section 3.1 in [7]). The Dynamic Identification and Research Group (DIRG) of Politecnico di Torino provided, from this test bed, a huge dataset with two main subsets (i.e., 46,592,000 samples \(\times\) 6 channels and 54,067,200 samples \(\times\) 6 channels, respectively) [7]. The first subset, \"Variable speed and load\", includes tests under different working conditions up to 30,000 rpm and tests with different types and levels of damage and is mainly dedicated to diagnosis studies. The second one is the \"endurance\" set, comprising experiments verifying the speed of damage severity under standard operating conditions of load and speed and is dedicated to prognosis studies. Accordingly, six channels with duration \(T\) and sampling frequency \(f_{s}\) were collected. Each time-domain acceleration record consists of \(T\times f_{s}\)s samples, while the values of \(T\) and \(f_{s}\) are given differently for the two investigations performed on the bearings. The data were collected using accelerometric acquisitions at variable rotational speed, radial load, and level of damage, as well as temperature measurements. Specific accelerometer sensitivity values were set in the OR38 signal analyzer, so the files contain acceleration time histories in \(\text{m}/\text{s}^{2}\). The accuracy of the OR38 input channels is as follows: phase of \(\pm\)0.02\({}^{\circ}\), amplitude of \(\pm\)0.02 dB, frequency of \(\pm\)0.005%. The introductory paper [7] does not explicitly mention any potential sources of bias or limitations in the dataset. However, it is important to note that the data were collected under specific working conditions and may not be representative of all possible scenarios. Additionally, the analysis techniques used in the paper may have limitations and may not be applicable to all types of data. This work uses both subsets to test the capability of the proposed data engineering scheme associated with the SCIDTL methodology. Accordingly, this section is dedicated to describing the most important features of the dataset and its processing steps while passing through some important illustrative examples.  
Figure 1: Overview of the test rig and its main components: (**a**) the test rig; (**b**) accelerometers’ and reference system’s positions; (**c**) roller bearing shaft. Reproduced from [7], Elsevier: 2019.