Uncertainty quantification and stochastic response prediction for excavator systems under extreme operating conditions

Abstract

The quantification of extreme operating conditions and their corresponding response predictions is a considerable challenge regarding the operation and maintenance of electromechanical equipment, such as dynamic excavator systems. This paper proposes a novel nonstationary modulation function model based on kinematic and dynamic analyses driven by on-site measurement data acquired from excavators. Extreme conditions are simulated through Gaussian white noise and impulse functions, and harmonic functions are overlaid on them to fit uncertain excitations, thus quantifying real random excitations. A key component of this method is its detailed statistical analysis of the probability density function (PDF) of the system response via Monte Carlo simulation. The results show that, compared with the measured results, the mean error of the system response PDFs predicted by this methodology is extremely low, and the developed approach can effectively capture the heavy-tailed characteristics of random responses under extreme loads. The proposed method utilizes a limited number of physical experiments to generate statistical results for large samples, providing the dual benefit of cost-effectiveness and high accuracy.