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The feature of correlation analysis were described and applied to analyzing the vibration signal of the gearbox. Aiming to that the diagnosis effect of the rolling bearings incipient fault was not good through the vibration spectrum and the resonance demodulation spectrum directly, the information fusion technology based on the correlation analysis is proposed to processing the vibration and acoustic resonance demodulation signal. The experimental results show that the presented correlation fusion analysis technology can be as the basis of the effective fault diagnosis method for the rolling bearings incipient defect.

A framework of modeling and simulating a typical artificial system is proposed based on discrete event system and Petri net. Firstly, the system model is constructed based on discrete event system theory. Secondly, the model is described and analyzed by using Petri net. Then, the simulation procedures on Matlab platform are presented in detail. The proposed framework is applied to modeling and simulating a logical control system of a marine diesel engine. The simulation results indicate that this logical control system model can be constructed by the given framework and the proposed method is effective in simulating and analyzing this kind of artificial system.

Holospectrum is the spectrum analysis technique which synthesizes the information of the phase, amplitude and frequency. Firstly, the features of the 2-demensional holospectrum were analyzed through the simulating calculation. Secondly, the application of two spectrum correction methods, the phase difference and interpolation method, in the 2-demensional holospectrum technique was discussed respectively. And the result indicates that the former method has better effect for improving the precision of the sine coefficient, the cosine coefficient and the eccentricity. At the end of the paper the 2-demensional holospectrum technique combined with the phase difference method was used to detect three kinds of faults in a multi-functional rotor test rig and the good experimental result was gained.

It was difficult for the traditional analytical method to analyze the acoustics characteristics of acoustics sensors. In order to solve the problem, a method based on the Finite Element Method (FEM) was investigated combining with the technology of harmonic response analysis. After presenting the basic math method theory of finite element, the harmonic response analysis model of ultrasonic liquid level transducer was simulated by ANSYS software package, and its corresponding acoustics parameter was illustrated. The simulation results showed that the ultrasonic probe has a good directionality and its resonant frequency is 1.01MHz; the energy of the acoustic beam is focused on the clearance of probe between transmit and receive; in the design for the transducer, the probe should be mounted in the region of maximum pressure amplitude of underwater ground field.

An intelligent fault diagnosis method based on principal component analysis (PCA) and least squares support vector machines (LS-SVM) is proposed. The characteristic parameter set is obtained by wavelet packet transform (WPT). And PCA is used to extract the principal features associated with the diagnosing object. Then, the training data set which is reduced from the original parameters are used as inputs to a LS- SVM for founding the classifier. In the paper, the PCA and LS- SVM method successfully realizes the multi-class failure recognition on the centrifugal pump circulation system. The experimental results demonstrate that WPT based characteristic parameters construction method and PCA based feature extraction technology are effectively, and the LS-SVM algorithm using the RBF kernel function had good multi-classification properties.