# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
## 3 Experimental Design and Verification  
### Dataset Description and Preprocessing  
Dataset 1: The bearing wear dataset used in this paper is obtained from a parts quality monitoring center located in Changchun, Jilin Province, China. This institution has a collaborative relationship with the School of Computer Science and Technology at Changchun University of Science and Technology. All the data were collected on the experimental platform provided by this institution. The experimental platform is shown in Figure 4. Part of the data set sequence is shown in Figure 5.  
The experimental bearings used are double-row radial ball bearings (SKF 2204E-2 RS1 TN9). An AC motor rotates at a constant speed of 3000 RPM and is connected to the bearings through a friction belt. The bearing material is high-carbon chromium bearing steel (GCr15) with an outer ring diameter of 47 mm, an inner ring diameter of 20 mm, a radial load of 11.5 kN, and a torque load of 0.7 N\(\cdot\)m. The collected data consists of nine features: vertical force, jaw angle, torque, torque peak value, torque valley value, friction coefficient, bearing temperature, operating time, and wear amount. Torque is measured using a torque sensor with a range of 500 N\(\cdot\)m. The jaw angle is measured using a wire encoder, and angular displacement (angular velocity) is detected using an optical encoder. The bearing wear amount is measured using a laser distance sensor. The force on the bearing is measured using a load sensor. The sampling frequency is set at 20 kHz, with measurements taken at regular intervals to capture the bearing wear amount. The collected data is divided into a 70% training set and a 30% testing set. This dataset is utilized in both Experiment 1 and Experiment 2. Figure 3: ARIMA Model Construction Process. Dataset 2: NASA Milling Cutter Wear Dataset [29]. This data is sourced from publicly available milling cutter wear experimental data conducted by NASA laboratories. The data collection was performed during dry milling and roughing operations under various cutting parameters and conditions. The experiment utilized six sensors, including two vibration sensors, two current sensors, and two acoustic emission sensors. The collected data consists of six features: table vibration, spindle vibration, AC spindle motor current, DC spindle motor current, acoustic emission at the table, and acoustic emission at the spindle. The sampling frequency was set at 100 kHz, resulting in a total of six-channel signals. A total of sixteen 6-flute milling cutters (KC710) were tested in the experiment. Each cutter was machined in a brand-new state, and the tool wear was measured using a microscope at regular intervals until the wear reached a certain level and the experiment was stopped. Two feed rates, 0.5 mm/rev and 0.25 mm/rev, which translate into 413 mm/min and 206.5 mm/min, and two radial cutting depths, 0.75 mm and 1.5 mm, were selected as milling parameters. The experiment included two types of tool materials: cast iron and stainless steel. This paper selected data from tools 3, 9, and 13 and created three test cases, C3, C9, and C13, respectively, for method validation, where C3 and C9 were used as the  
Figure 4: Illustration of experimental platform. Figure 5: Illustration of wear prediction sequence dataset. training sets and C13 was used as the testing set. This dataset is utilized in Experiment 3. Table 1 represents the processing conditions corresponding to three types of cutting tools. As mentioned in the introduction of this paper, industrial big data has many uses and can be applied in industrial modeling, prediction, control, decision-making, optimization, fault diagnosis, etc. However, the pursuit of stability and reliability in the industry sets high requirements for data quality in these applications. Specifically, data generated in industrial processes is prone to noise, missing values, and outliers due to sensor failures, human operational factors, and system errors.