Abstract:
The Centrifugal compressor is a piece of key equipment for petrochemical factories. As the
core component of a compressor, the blades suffer periodic vibration and flow induced excitation
mechanism, which will lead to the occurrence of crack defect. Moreover, the induced blade defect
usually has a serious impact on the normal operation of compressors and the safety of operators.
Therefore, an effective blade crack identification method is particularly important for the reliable
operation of compressors. Conventional non-destructive testing and evaluation (NDT&E) methods
can detect the blade defect effectively, however, the compressors should shut down during the testing
process which is time-consuming and costly. In addition, it can be known these methods are not
suitable for the long-term on-line condition monitoring and cannot identify the blade defect in time.
Therefore, the effective on-line condition monitoring and weak defect identification method should
be further studied and proposed. Considering the blade vibration information is difficult to measure
directly, pressure sensors mounted on the casing are used to sample airflow pressure pulsation signal
on-line near the rotating impeller for the purpose of monitoring the blade condition indirectly in
this paper. A big problem is that the blade abnormal vibration amplitude induced by the crack is
always small and this feature information will be much weaker in the pressure signal. Therefore, it is
usually difficult to identify blade defect characteristic frequency embedded in pressure pulsation
signal by general signal processing methods due to the weakness of the feature information and the
interference of strong noise. In this paper, continuous wavelet transform (CWT) is used to pre-process
the sampled signal first. Then, the method of bistable stochastic resonance (SR) based onWoods-Saxon
and Gaussian (WSG) potential is applied to enhance the weak characteristic frequency contained
in the pressure pulsation signal. Genetic algorithm (GA) is used to obtain optimal parameters for this SR system to improve its feature enhancement performance. The analysis result of experimental
signal shows the validity of the proposed method for the enhancement and identification of weak
defect characteristic. In the end, strain test is carried out to further verify the accuracy and reliability
of the analysis result obtained by pressure pulsation signal.
