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 measurements at selected locations are calculated in a piecewise manner by dividing the acceleration-time history into pieces. Following, Frequency Response Functions (FRF's) whose outputs are the stress values at selected components are calculated by finite element methods. Then, fatigue damage at selected output locations is estimated using the FRF results. To this end, the following frequency domain fatigue estimation methods proposed for Gaussian and stationary data sets are applied to the selected components of a heavy duty truck: narrow band approximation, Benasciutti and Tovo, Zhao and Baker, Benasciutti and Tovo's α0.75 and Dirlik methods. 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 PSDs are updated accordingly. Then, spectral moments and fatigue damage intensities are calculated in frequency domain. Implementation of the proposed approach is described in detail and numerical results are presented. In addition, spectral moments and fatigue damage intensities are stored in the memory of electronic control unit(ECU) to monitor loading history of vehicle with corresponding time intervals to determine at which time interval the most fatigue damage occurred. It is shown that the proposed approach is able to predict the fatigue damage accurately at reasonable CPU time and can keep track of loading conditions in real time.