Real time high cycle fatigue estimation algorithm and load history monitoring for vehicles by the use of frequency domain methods

Real time high cycle fatigue estimation problem for vehicles is examined by the use of frequency domain methods. The purpose was twofold: monitoring of fatigue damage and tracking of load history in real time. Firstly, power spectral density functions (PSDs) of acceleration measurement at a selected location are calculated in a piecewise manner by dividing the acceleration-time history into pieces. Following, Frequency Response Functions (FRF's), whose outputs are the absolute maximum principal stress values at selected components, are calculated by finite element methods to account for multi-axial stress state in fatigue life estimations. Then, fatigue damage intensity at selected output locations is estimated using the FRF results. To this end, the following frequency domain fatigue estimation methods (FDFEMs) proposed for Gaussian and stationary data sets are applied to the selected components of a heavy duty truck: narrow-band approximation, Tovo and Benasciutti, Zhao and Baker, Dirlik and Tovo's α_0.75 methods. Gaussianity and stationarity of acceleration-time data set used to estimate fatigue life of a component is checked to ensure the validity of FDFEMs. Numerical results are compared with experimental fatigue lives and damage calculations in time domain made by the combination of rainflow counting and Miner-Palmgren rules. There are two difficulties in implementing this approach using on-board equipment in real time such as overcoming the limited memory to store data sets and completing the computations sufficiently fast. To overcome them, the proposed approach is implemented in a piecewise manner and associated normalized PSDs are updated accordingly. Then, spectral moments and damage intensities are calculated in frequency domain. Implementation of the proposed approach is described in detail and numerical results are presented. It is shown that the proposed approach is able to predict the fatigue damage accurately and can keep track of loading conditions in real time.