Abstract
Article aims to determine the most effective machine learning model for clustering vibration diagnostics data. The research includes analysis of various models and methods such as-means, Agglomerative Clustering, TimeSeriesKMeans and CatBoost. The goal is to select a method that can best identify the data structure and improve understanding of the characteristics of vibration signals. The results of the study can be useful for the development of effective monitoring and diagnostic systems for equipment, as well as for improving the reliability and performance of technical systems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
