PREDICTION OF MATERIAL WEAR BASED ON MACHINE LEARNING: A COMPARATIVE ANALYSIS OF ALGORITHMS
TL;DR
A comparative analysis of the effectiveness of various machine learning methods for predicting the wear of contacting parts in tribological systems identifies the most suitable machine learning techniques for specific operational conditions, providing valuable insights for the development of intelligent predictive maintenance systems aimed at enhancing the reliability of friction units.
Abstract
The article is devoted to a comparative analysis of the effectiveness of various machine learning methods for predicting the wear of contacting parts in tribological systems. With the increasing demand for reliability and longevity of industrial equipment, accurate wear forecasting has become a critical task. The study evaluates a range of algorithms, from classical supervised learning methods like regression and support vector machines to advanced deep learning architectures. The core of the research involves the processing of experimental wear data, feature selection, and hyperparameter optimization to build robust predictive models. The performance of these models is compared based on key metrics such as accuracy, generalizability, and computational efficiency. The results identify the most suitable machine learning techniques for specific operational conditions, providing valuable insights for the development of intelligent predictive maintenance systems aimed at enhancing the reliability of friction units.