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Numerical models for vibro-acoustic analyses of complex mechanical systems are becoming more and more popular, in particular in the field of virtual product development. Therefore, reliable, comprehensive, and validated modeling methodologies remain crucial. However, system characteristics such as elasticities of the drive train components and nonlinear characteristics can lead to complex, and costly numerical models with a huge number of degrees of freedom. This may raise not only the need for novel and reasonable modeling strategies, but also exacerbates validation process, due to the wide scope in terms of operating conditions. In practice, structure-borne noise signals, for example, from accelerometers, are often used for the validation of mechanical systems. By choice of a sufficient number of measurement points, the interpretation becomes more complex. A lot of vibration response curves then need to be compared and interpreted over a wide operating range. In general, the interpretation focuses on deviations in quality and quantity. In this paper, to overcome these mentioned challenges, a validation methodology is proposed allowing a fast and transparent check of a number of captured signals. Therefore, it is shown how the original information can be reduced in a meaningful manner, making it possible to run a fast and accurate validation. The method is demonstrated on a real application with high mechanical complexity and it is shown that the chosen parameters are reliable.

The present study aims to combine the fields modal analysis and signal processing and to show the use of Frequency Response Function (FRF), as a vibration transfer path, in enhancing reliability and abilities of the next generation vibration-based rolling bearing condition monitoring (CM) systems in complex mechanical systems. In line with this purpose, the hereby-presented paper employs an appropriate numerical model, that is, Multibody Simulation (MBS) of a vehicle's drivetrain as a manner for numerical modal and structural analyses. For this, first, the principles of vibration-based bearing fault detection are reviewed and presented. Following that, a summary of MBS modelling and validating strategies are given. Then, the validated MBS model is used as a case study for further investigations. The results can confirm existence of challenges in fault detection of rolling bearings, in particular in complex mechanical systems. In further discussions, the capability of FRFs in fault localization and determination of ideal sensor positions is discussed in some detail. Finally, concluding remarks and suggestions for future works are summarized.

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