In this paper, 20 finite element method is used to calculate the static characteristics of switched reluctance motor, such as the electromagnetic normal force, flux-linkage and torque-anglecurrent characteristics. The system model is build to study the vibration dynamic response which is excited by electromagnetic force. Because vibration is a major problem of switched reluctance motor drive system, which can cause undesirable acoustic noise and acoustic noise is severe when the periodic excitation frequency of normal force near the natural vibration frequency of the stack. So, it is very important for calculating the periodic excitation frequency to avoid the resonant vibration. The electromagnetic normal force is calculated and applied to the motor system, and FFT is used to analyse the dynamic vibration response of the switched reluctance motor drive system. In the end, a prototype motor is build and the numerical analysis is appl ied to a four phase 8/6 poles switched reluctance motor to obtain the key frequency 7450Hz of the acceleration response.
The load forecast is the foundation of optium control for heating system.This paper systematicaly discussed the application research of heating system predication which adopted the fuzzy neural networks technology. RBF neural networks are constructed by MATLAB.This method is characterized by higher computing accuracy and fast convergence velocity.it is very suitable in the engineering and may greatly ehance the automation of central heating system and energy-saving effects.
The radio altimeter system is an important part of the aircraft navigation system, the system failure will directly affect flight safety. Therefore, to ensure the safe flight of the aircraft, a fault diagnosis method based on fault trees and Bayesian networks was proposed in this paper. Firstly, starting from the system composition and working principle, taking the typical failure of the system ‘loss of radio altitude displays’ as an example, the fault trees were set up to comprehensively analyze the cause of the fault. On this basis, the built-in fault tree models were used to build the Bayesian network model, and to determine the maximum possible paths and key nodes of the fault through reverse reasoning to achieve the fault positioning. The outcomes demonstrate the excellent accuracy and dependability of the proposed method.
This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine
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