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On the study of reducing the disturbance on driver’s attention induced by low frequency vehicle interior stationary noise, a subjective evaluation is firstly carried out by means of rank rating method which introduces Distraction Level (DL) as evaluation index. A visual-finger response test is developed to help evaluating members better recognize the Distraction Level during the evaluation. A non-linear back propagation artificial neural network (BPANN) is then modeled for the prediction of subjective Distraction Level, in which linear sound pressure RMS amplitudes of five Critical Band Rates (CBRs) from 20 to 500Hz are selected as inputs of the model. These inputs comprise an input vector of BPANN. Furthermore, active noise equalization (ANE) on DL is realized based on Filtered-x Least Mean Square (FxLMS) algorithm that controls the gain coefficients of inputs of trained BPANN.

In this paper the approach of using modal parameters to detect and locate damage of automobile rear axle under experimental condition is explained. This method uses the changes in the curvature mode of the structure as the damage identification indicator to detect and locate damage. The curvature mode and the damage identification indicator are explained, the process of the identification is introduced. The method is demonstrated with a FEM (Finite Element Method) analysis on a plate under different damage conditions. And the indicator is improved with a weighting function. Then EMA (Experimental Modal Analysis) is conducted on a damaged and an undamaged rear axle of a vehicle to get the modal parameters for the damage identification indicator which later identifies and locates the damages, thus validating the introduced method.