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In view of the steam turbine vibration signal being vulnerable to more complex random noise pollution, it puts forward an improved particle filter method of vibration signal de-noising. First, it establishes the mathematical model of the vibration signal acquisition as the state equation in the particle filter. Then wavelet analysis is used to extract the background noise of the signal acquisition. The background noise and the state signal are used as the observation signal, and the observation equation is obtained. It converts into the problem under state space model. Because there is a sample degradation problem in the particle filter using the sequential importance sampling. In the re-sampling stage, it proposes a re-sampling algorithm of weight sorting and survival of the fittest. It normalizes weight of each particle from small to large. It eliminates the large variance of the particles and keeps the small variance of the particles when their weights are equal. To a certain extent, the improved particle filter algorithm solves the degradation problem of the particle filter. The improved particle filter algorithm is applied to the vibration fault signal. The signal and de-noised signal are decomposed by the wavelet packet to obtain the frequency band energy. According to the change of each frequency band energy extracts fault parameter. The fault symptoms condense the whole information of turbine vibration faults. By comparing the recognition rate of the signal and de-noised signal through different fault diagnosis method, the improved particle filter is proved to be better in the fault diagnosis of steam turbine.