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In the earth-moving industry manymachines work in typical loading cycles that are repeated periodically. For a statistic examination of the overall load configuration and the dynamic fatigue of these machines, it is necessary to develop an adaptive algorithm for the separation of the individual cycles. This article presents methods for an automatic detection of the cycle borders. Adaptive algorithms are constructed for a reliable separation at different points during the loading cycle. Additionally, each cycle can be divided into different operating phases by extending the algorithms to a tool for the identification of each single phase. To avoid problems during the cycle detection, the data are checked for outliers and sensor faults first. To guarantee a meaningful statistical analysis, the separated cycles have to be tested for incorrect or atypical characteristics. Therefore, statistical classification numbers are calculated and compared for each cycle.

A shortcoming of widely-used integral methods for prediction of flow-induced sound emission of rotating systems is that the rotation of the impeller can be included in the calculation, but not reflections of sound from the housing, rotor blades and attached ducts. This paper introduces a finite element method that correctly maps both the sound sources rotating with the impeller and the reflections of the sound from the rigid surfaces of the components of the blower. For the prediction of flow-induced sound a hybrid approach is employed using separate CFD and acoustic simulations. It is based on a decomposition of flow (incompressible part) and acoustic (compressible part) quantities and is applicable to high-Reynolds-number and low-Mach-number flows. It features only a scalar unknown (i.e. the acoustic velocity potential), thus reducing the computational effort significantly.