Machine Learning for Modeling Service Life: Comprehensive Review, Bibliometrics Analysis and Taxonomy

This article presents a comprehensive review of the machine learning methods used to model the service life of various products, which is a critical aspect of product development and production. With the recent advances in machine learning, it has become increasingly feasible to utilize these methods for modeling service life accurately. This review provides a detailed examination of the existing literature on machine learning applications for modeling service life, including a bibliometric analysis of the most frequently cited works. Furthermore, this review presents a taxonomy of the various machine learning methods employed in service life modeling, highlighting the fundamental methods such as Artificial neural networks, Support vector machine, and decision trees. The results of this review demonstrate the potential of machine learning methods for accurately modeling service life, while also emphasizing the need for further research in this field. Overall, this article provides a valuable resource for researchers and practitioners looking to apply machine learning methods for modeling service life.