In this paper, the hazards of hydraulic pipeline system vibration and noise were introduced, the causes of vibration and noise of hydraulic pump circuit were analyzed and its frequency components were discussed. A wave filter of structure resonator based on Fluid-structure coupling vibration is designed and manufactured according to the principle of gas muffler. The differential equation of rectangular plate transverse free vibration characteristics was established, the coupling vibration resonance frequency of filter structure vibration body was obtained about 218Hz by solving differential equation according to the Rayleigh - Ritz method. The hydraulic pump flow pulsation frequency was adjusted respectively 215.55 Hz and 197.4 Hz through frequency conversion motor adjustment, and then comparative experiments were tested on the hydraulic system pressure fluctuation test experimental platform. Experiments show that, when the inherent resonance frequency of filter structural vibration body was approached the hydraulic system frequency, system pulse energy would be effectively attenuated, system pressure fluctuations would be greatly reduced and pulse rate was dropped from 3.65％to 0.28％. The attenuation of the effects of pressure pulsations was validated through experimental research, but also its defects that the filter has frequency selectivity and band narrow were founded The later research direction was put forward and new technology for hydraulic system vibration control was provided.

In order to improve reliability of excavator’s hydraulic system, a fault detection approach based upon dynamic general regression neural network (GRNN) approach was proposed. Dynamic GRNN is an extension of GRNN, which could effectively caputure the dynamic behavior of the nonlinear process. With this approach, normal samples were used as training data to develop a dynamic GRNN model in the first step. Secondly, this dynamic GRNN model performed as a fault determinant of the test fault. Experimental faults were used to validate the approach. Experimental results show that the proposed fault detection approach could effectively applied to the excavator’s hydraulic system.

According to structure of traditional milling machine, the tooth surface error identification model of forming processing spiral bevel gear is established by the 4×4 Denavit-Hartenberg homogeneous transformation matrix, the gear meshing theory and so on. In view of the present commonly used least squares method’s solution defects, the truncated singular value decomposition (TSVD) and the L curve method are proposed to solve the identification model and the solution method accuracy was verified by the example and experiment. The results show that the gear concave average error is at 0.01498mm before correction, and the average error will drop down to 0.00084mm after using this method correction, and the error corrects 94.4%. The gear convex average error is at 0.00846mm before correction and the average error will drop down to 0.00176mm after using this method correction and the error corrects 79.2%.

In order to improve reliability of the excavator’s hydraulic system, an online fault detection approach based on dynamic principal component analysis (PCA) was proposed. With this approach, normal samples were used as training data to develop a dynamic PCA model online with new data. Secondly, T2 statistic and Q statistic performed as indexes of online fault respectively. Several experimental faults were introduced to validate the approach, and the dynamic PCA model developed were able to detect overall faults using T2 statistic and Q statistic. By experiment analysis, the proposed approach achieved an accuracy of 95% for 20 test samples. Experimental results shows that the online fault detection approach could effectively applied to the excavator’s hydraulic system.

No abstract