A new modal decomposition algorithm based on PSO and spectrum segmentation

A new modal decomposition algorithm using particle swarm optimization for optimal vibration signal spectrum modal decomposition in this paper. The modal parameters of vibration signal are elements of particle, and at the end the best particle will be found based on PSO algorithm. There are two key problems to be solved. The first problem is how to estimate initial modal parameters of vibration signal, and the second problem is how to prevent the local optimization particle. This paper introduces the idea of clustering the spectrum to various estimates of the modal parameters of the maximum and minimum levels, thus the attribute value is given particle upper and lower bounds, reducing the particle swarm algorithm search space. At the first, the spectral curve is looked upon as a set of small local peaks, the clustering distance function is constructed, and the k-means algorithm is used to automatically cluster vibration signal spectrum into single-mode categories. Then a single-mode decomposition algorithm is used to estimate each modal parameters. This paper also uses a hybrid variant particle swarm algorithm to avoid falling into local optimization, to improve the efficiency and accuracy. By the amount of experimental results of simulation signals, compared to the classic Levy algorithm, the algorithm in this paper is stronger noise resistance and more stable.