No abstract

No abstract

In this article, the improvements on vibration isolation performance of hydraulic excavators are achieved via the optimization of powertrain mounting system. The powertrain is viewed as a rigid body and described by a 6-degree-of-freedom model. The rigid-flexible coupling model of hydraulic excavators is carried out based on software ADAMS, in which the influences from the mass and elastic deformation of base are considered. In the process of optimization for the powertrain mounting system, energy decoupling rate and vibration transmissibility are set to be the objective functions, while the stiffness coefficients in three directions of the mounting coordinate systems are chosen as the designed variables. With the given constrained conditions of these variables, nondominated sorting genetic algorithm II is employed to optimize the stiffness coefficients of suspension elements. The simulations for the rigid-flexible coupling model with the optimized mounting system show that the vibration isolation performances of hydraulic excavators are improved comparing with that with non-optimized powertrain mounting system.

Operational modal analysis (OMA) is a powerful vibration analysis tool and widely used for structural health monitoring (SHM) of various system systems such as vehicles and civil structures. Most of the current OMA methods such as pick-picking, frequency domain decomposition, natural excitation technique, stochastic subspace identification (SSI), and so on are under the assumption of white noise excitation and system linearity. However, this assumption can be desecrated by inherent system nonlinearities and variable operating conditions, which often degrades the performance of these OMA methods in that the modal identification results show high fluctuations. To overcome this deficiency, an improved OMA method based on SSI has been proposed in this paper to make it suitable for systems with strong nonstationary vibration responses and nonlinearity. This novel method is denoted as correlation signal subset-based SSI (CoS-SSI) as it divides correlation signals from the system responses into several subsets based on their magnitudes; then, the average correlation signals with respective to each subset are taken into as the inputs of the SSI method. The performance of CoS-SSI was evaluated by a simulation case and was validated through an experimental study in a further step. The results indicate that CoS-SSI method is effective in handling nonstationary signals with low signal to noise ratio (SNR) to accurately identify modal parameters from a fairly complex system, which demonstrates the potential of this method to be employed for SHM.