Reliability Design for Impact Vibration of Hydraulic Pressure Pipeline Systems

The research of reliability design for impact vibration of hydraulic pressure pipeline systems is still in the primary stage,and the research of quantitative reliability of hydraulic components and system is still incomplete.On the condition of having obtained the numerical characteristics of basic random parameters,several techniques and methods including the probability statistical theory,hydraulic technique and stochastic perturbation method are employed to carry out the reliability design for impact vibration of the hydraulic pressure system.Considering the instantaneous pressure pulse of hydraulic impact in pipeline,the reliability analysis model of hydraulic pipeline system is established,and the reliability-based optimization design method is presented.The proposed method can reflect the inherent reliability of hydraulic pipe system exactly,and the desired result is obtained.The reliability design of hydraulic pipeline system is achieved by computer programs and the reliability design information of hydraulic pipeline system is obtained.This research proposes a reliability design method,which can solve the problem of the reliability-based optimization design for the hydraulic pressure system with impact vibration practically and effectively,and enhance the quantitative research on the reliability design of hydraulic pipeline system.The proposed method has generality for the reliability optimization design of hydraulic pipeline system.

Intelligent Diagnosis of Short Hydraulic Signal Based on Improved EEMD and SVM with Few Low-dimensional Training Samples

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD（empirical mode decomposition） and EEMD（ensemble empirical mode decomposition） are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM（support vector machine）, extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD＋SVM method is compared with the EEMD＋SVM and EMD＋SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.