The fault diagnosis accuracy of a working automaton can be improved when the interfering signals and noise are removed from the complex vibration signal under multi-interference and heavy noise. The basic theory of ICA (independent component analysis) is studied, and ICA based on improved particle swarm optimization is introduced into the simulation. A comparatively satisfactory separation effect is obtained, and practical data shows that the approach is feasible.