# Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
###### Abstract  
In the prediction and modeling analysis of wear degree in the field of industrial parts processing, there are problems such as poor prediction ability for long sequence data and low sensitivity of output feedback to changes in input signals. In this paper, a combined prediction model is proposed that integrates dual attention mechanisms and self-regressive correction. Firstly, pre-processing is performed on the collected wear data to eliminate noise and aberrant mutation data. Then, the feature attention mechanism is introduced to analyze the input data sequence, and the weights of each feature under the temporal condition are set based on the contribution of the prediction results, thereby obtaining the LSTM hidden state at the current time. Subsequently, the temporal attention mechanism is introduced to perform a weighted calculation of the hidden state information, analyze the correlation of long-term sequential wear data, and decode and output the analysis results. Finally, the ARIMA model is used to perform linear correction on the predicted results to improve the accuracy of wear degree prediction. The proposed model is compared and analyzed with the models that are highly related in recent research on real-world wear degree datasets. The experimental results show that the improved model has a better ability to improve the corresponding problems and has a significant increase in prediction accuracy. attention mechanism; time series; ARIMA model; combined prediction 202313902639023390239  
a decline in the precision and quality of produced parts, causing significant economic losses to factories [4]. Predicting the degree of component wear is a crucial step towards industrial production intelligence. Accurate wear prediction can provide reliable references for adjusting the factory's processing methods and serve as a basis for formulating new processing plans, giving the production process a certain level of foresight. Additionally, precise wear prediction can proactively warn against abnormal increases in wear, enabling factories to mitigate potential loss risks, save production costs, and enhance productivity [5]. Therefore, research on predicting component wear has significant practical implications. The prediction of wear degree in material components is based on a large amount of historical data from the machining process to forecast future wear conditions. There are many factors that contribute to component wear, such as machining duration, vertical force, jaw rotation angle, torque, coefficient of friction at the contact surface, temperature, etc. It can be seen that the wear data collected from the production line is a set of time-sequential data composed of multiple variables. Therefore, wear degree prediction involves the study of multivariate time series forecasting methods [6]. The research on wear degree prediction algorithms conducted in this paper is based on existing time series forecasting algorithms. The initial research on time series prediction used traditional statistical econometric methods for modeling, including autoregressive models (AR), moving average models (MA), autoregressive moving average models (ARMA), and autoregressive integrated moving average models (ARIMA) [7], etc. [PERSON] et al. [8] proposed a structural damage identification method based on time series analysis that uses the ratio of the residual of the identified working condition to the variance of the residual of the AR prediction reference model as the damage indicator to identify structural damage. [PERSON] et al. [9] constructed multiple original data matrices based on the acceleration AR model coefficient vectors under non-damaged and unknown states using structural acceleration time series to identify structural damage. [PERSON] et al.