The application discloses a
compressor stall failure acoustic identification method and
system, and relates to the field of engine state monitoring and fault diagnosis. The method comprises the following steps: collecting acoustic signals in the operation process of the compressor; performing frame division and windowing
processing on the acoustic signals; performing
spectrum analysis and
frequency band division on each frame of windowed signals to generate a frame energy vector; evaluating the importance of each
frequency band based on the
random forest out-of-bag
replacement method to screen out key frequency bands; constructing a triangular
filter bank with each frequency in the key frequency set as a vertex, and performing neighborhood expansion when the interval between adjacent frequencies exceeds a threshold interval to obtain a reconstructed
adaptive filter bank and a final filter energy
feature vector; and inputting the final filter energy
feature vector into a deep neural
network model to obtain an identification result of whether the compressor is in a stall state. The application has the advantages of reducing equipment cost, simplifying installation process, improving stall feature targeted analysis capability, and enhancing the robustness of the identification result.