Abstract: The combination of fault diagnosis methods based on genetic algorithm for rotating machinery is presented, as there exists limitness for any single fault feature, any single diagnosis method to achieve the accurate diagnosis needs the whole diagnosis state area. This method can effectively use diversified different fault character and diagnosis methods that can present their advantage respectively, so that the diagnosis accuracy is improved. Neural network diagnosis method and artificial immune system diagnosis method are combined by using genetic algorithm. Two different characters, Wavelet Packet” energy” character and Bispectrum character, are used. After training the two fault diagnosis methods, the genetic algorithm is used to optimize diagnosis combination weight matrix. It is demonstrated from the diagnosis example of rotating machinery that the combination diagnosis method can improve the accuracy rate and diagnosis system robust quality effectively.

Key words: Artificial immune system, Combination diagnosis, Genetic algorithm, Rotating machinery