[2F5] A novel high-resolution dynamic model for bearing analysis using MSC Adams and MATLAB integration in order to research the dynamic behaviour
O Schwarcz
This paper presents a novel dynamic model for bearing analysis using MSC Adams, addressing the significant computational challenges typically encountered in such simulations. The model enables a high-resolution output in both the frequency and time domains, utilising a large number of small time steps. This approach effectively mitigates transient effects, resulting in accurate and reliable simulations, streamlining the overall workflow and enhancing computational efficiency.
The model employs MATLAB to generate scripts that create, execute and post-process the simulations in MSC Adams. This integrated method not only simplifies the modelling process but also ensures precise simulation results. The data obtained by simulations is labelled and shall be used as a database for the development of machine learning-based diagnostic tools.
The presentation includes the following: a method of performing high-resolution simulations overcoming the demand for high computational resources; a validation process of a bearing model; and a discussion of research results and insights.
The primary application of this model is to investigate the effect of defect severity on the dynamic behaviour of bearings. Through extensive simulations, the study examines how varying defects impact bearing performance, providing crucial insights for predictive maintenance and design optimisation.
The methodology, application and validation of the model are thoroughly discussed, demonstrating its capability and reliability. The results underscore the potential of the model as a valuable tool for detailed bearing analysis and its broader implications for mechanical system diagnostics.
The model employs MATLAB to generate scripts that create, execute and post-process the simulations in MSC Adams. This integrated method not only simplifies the modelling process but also ensures precise simulation results. The data obtained by simulations is labelled and shall be used as a database for the development of machine learning-based diagnostic tools.
The presentation includes the following: a method of performing high-resolution simulations overcoming the demand for high computational resources; a validation process of a bearing model; and a discussion of research results and insights.
The primary application of this model is to investigate the effect of defect severity on the dynamic behaviour of bearings. Through extensive simulations, the study examines how varying defects impact bearing performance, providing crucial insights for predictive maintenance and design optimisation.
The methodology, application and validation of the model are thoroughly discussed, demonstrating its capability and reliability. The results underscore the potential of the model as a valuable tool for detailed bearing analysis and its broader implications for mechanical system diagnostics.
