Power plant equipment condition monitoring based on sound signal is a new type of equipment condition fault diagnosis technology. The high noise environment of power plant is the technical difficulty to be solved in the application of this technology. Aiming at solving the problem of sound recognition of power plant equipment in high noise environment

a fusion feature generation method of power plant equipment sound based on Mel frequency cepstrum coefficient(MFCC) and gamma tone frequency cepstrum coefficient(GFCC) is proposed to realize accurate recognition of different equipment operation sounds. Firstly

the 96-channel sound imager is used to collect the sound signals of 6 kinds of equipment in operation in the high noise environment of the power plant

and preprocess the sound signals to obtain multi-frame sample signals. Then

MFCC and GFCC are applied to extract the features of the preprocessed audio signal to obtain the original high-dimensional features. In order to eliminate the influence of noise and reduce the feature dimension

principal component analysis(PCA)

de-noise automatic encoder(DAE) and t-SNE algorithm are respectively used to reduce the dimension of the original highdimensional features

calculate the separability criterion function value of the corresponding category

and fuse the reduced dimension features in the same dimension reduction method to obtain the fused features. Finally

Gaussian white noise is added to the data set

and support vector machine algorithm is used for equipment voice recognition based on the fusion features to verify the accuracy and robustness of the fusion features. The experimental results show that

the fusion features have both the accuracy of MFCC features and the robustness of GFCC features.Compared with the MFCC and GFCC feature extraction methods before and after dimension reduction

the recognition success rate has been significantly improved

which can provide a theoretical basis for the further research of power plant equipment condition monitoring and fault warning methods.