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The rolling bearing is a fundamental component of rotating machines and its failure may lead to a catastrophic damage of the system. The incipient and correct identification of faults contribute to an early predictive maintenance plan, which avoids additional costs and sudden breakdowns. The resonant demodulation technique, envelope analysis, is a well-established method widely used to identify failures in rolling bearings. However, this method requires the identification of the frequency region that contains enough information about the faults. Thus, the spectral kurtosis gives the impulsiveness measure of a vibration signal and it is used to identify the frequency region of failure. This paper presents the use of the bat algorithm as an optimization methodology to identify the resonant demodulation parameters using spectral kurtosis as the objective function.

One of the steps towards higher efficiency internal combustion engines (ICEs) is the application of new improved subsystems, with lower power consumption. One of such subsystems is the needle roller bearing valvetrain, where rolling bearings replace the common sliding bearings designs as camshaft supports, hence decreasing the frictional torque and increasing liquid power at the crankshaft. However, the first question to arise is the vibration characteristic of the system for the new design. In order to initiate the assessment of the vibration behavior of the camshaft, some fundamental investigations should be made, such as natural frequency identification. For that, one might benefit from virtually evaluate these characteristics via FEA / Rotordynamics algorithm, reducing the need for expensive experimental setups of the complete valvetrain. This work intends to assess the applicability of these both methods to the camshaft vibration problem.