Research Of Volterra Kernel Identification Method Based On IGA And Its Application In Cancellation Of Mechanical Vibration Signal Noise

Mechanical vibration noise is widely exist which caused a lot of interference for mechanical fault diagnosis which based on vibration signal. In order to improve the accuracy of fault diagnosis, it is necessary to filter the noise from the vibration signal in order to get effective vibration signal. In order to achieve this goal, many researchers have did a large number of studies on how to eliminate the noise signal of original signal, however, these studies unable to contain a variety of noise signal to restore the original signal which are carried out on a certain noise filtering. According to this phenomenon, this thesis puts forward the cancellation of mechanical vibration signal noise based on IGA of Volterra filter.This thesis is mainly introduces improved genetic algorithm for Volterra series kernel parameter of system,and deeply studies the identification method of Volterra kernel based on IGA and its application in cancellation of mechanical vibration signal noise. The results of simulation experiments of research and validate the certain findings. The primary contents of this thesis involve the following aspects:Firstly, select the appropriate swarm intelligence algorithm and put forward its improved algorithm. We finally determined choose using the genetic algorithm through the comparison and analysis on the properties of several common algorithms. Genetic algorithm is one kind of probabilistic search algorithm by simulate the biological evolution mechanism, which can quickly solve the complex nonlinear problems that the traditional optimization methods can’t match. However, the traditional genetic algorithm is also has the inevitable disadvantages,such as it’s easy to fall into local optimum and difficult to extricate themselves, sometimes the genetic operators which was given can’t meet the requirements of high-precision recognition.In order to improve the genetic algorithm we can use the following strategies: based on learning mechanism of opposites population initialization; According to the degree of the diversity of population adaptive change the genetic operators; With double population optimization, the first using GA and the second population using PSO; Use elite reserve strategy. Using improved strategy improved genetic algorithm, then testing the performance of IGA with the classical function Rosenbrock and Sphere, the testing results show that the proposed IGA has better optimization performance and optimum stability.Secondly, study the kernel parameters identification of Volterra series. Analysis the Volterra series model, and then gives the Volterra series kernel identification model by the previous research which will be studied. Using the IGA to identificate the kernel of Volterra series, and compared the result with the results of identification by genetic algorithm and particle swarm algorithm. The results show that the IGA has better accuracy and faster identification speed compared to traditional genetic algorithm and particle swarm algorithm.Thirdly, cancellation the noise signal from mechanical vibration signal by Volterra filter.Noise is one kind of pollution; and vibration noise which was too large, durable,multi-frequency noise can accelerate the wear parts, accelerated reported speed of the vehicle,the vibration noise also can make a produce great disturbance for researchers to study mechanical equipment and impossible to draw the right conclusions based on real situations.The first simulation experiment simulate the sine signals, then add Gaussian noise and impulse noise to the sine signal, finally filtering the sine signal which has Gaussian noise signal with Volterra filter, Weiner filter, Median filter and Mean filter. We can use genetic algorithms, particle swarm optimization and improved genetic algorithm optimized the kernel parameters of Volterra series When use Volterra filters to filtering the noise. We can easily conclusion that the proposed method has more excellent filtering effect, and stability,convergence speed, etc by using the results make a comparative analysis. The method also has a certain practicality vibration signal when used to filter the mechanical vibration signal which has hybrid noise signal with Volterra filter, Weiner filter, Median filter and Mean filter cancellation. We can use genetic algorithms, particle swarm optimization and improved genetic algorithm optimized the kernel parameters of Volterra series When use Volterra filters to filtering the noise. We can easily conclusion that the proposed method has more excellent filtering effect, and stability, convergence speed, etc by using the results make a comparative analysis, and the method also has a certain practicality.In summary, noise cancellation is one of the most successful applications for Volterra filter, the basic idea is to use algorithms to optimize the kernel parameter of Volterra series,then filtering the noise signal from the mechanical vibration signal with Volterra filter, the optimal kernel parameter of Volterra series can make the input noise signal the maximum degree of weakening and elimination when they through Volterra filter, and through the fitness function is judge to determine whether the noise canceling effect is the best. The proposed method has better performance compared to traditional method which can effectively filter the noise signal that mixed double Gaussian noise and impulse noise.