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Weak defect identification for centrifugal compressor blade crack based on pressure sensors and genetic algorithm

Journal Article


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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 on Woods-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.

UOW Authors


  •   Li, Hongkun (external author)
  •   He, Changbo (external author)
  •   Malekian, Reza (external author)
  •   Li, Zhixiong (external author)

Publication Date


  • 2018

Citation


  • Li, H., He, C., Malekian, R. & Li, Z. (2018). Weak defect identification for centrifugal compressor blade crack based on pressure sensors and genetic algorithm. Sensors, 18 (4), 1264-1-1264-19.

Scopus Eid


  • 2-s2.0-85045909689

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2323&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1321

Start Page


  • 1264-1

End Page


  • 1264-19

Volume


  • 18

Issue


  • 4

Place Of Publication


  • Switzerland

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 on Woods-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.

UOW Authors


  •   Li, Hongkun (external author)
  •   He, Changbo (external author)
  •   Malekian, Reza (external author)
  •   Li, Zhixiong (external author)

Publication Date


  • 2018

Citation


  • Li, H., He, C., Malekian, R. & Li, Z. (2018). Weak defect identification for centrifugal compressor blade crack based on pressure sensors and genetic algorithm. Sensors, 18 (4), 1264-1-1264-19.

Scopus Eid


  • 2-s2.0-85045909689

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2323&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1321

Start Page


  • 1264-1

End Page


  • 1264-19

Volume


  • 18

Issue


  • 4

Place Of Publication


  • Switzerland