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Sensors (Basel). 2018 Apr 19;18(4). pii: E1264. doi: 10.3390/s18041264.

Weak Defect Identification for Centrifugal Compressor Blade Crack Based on Pressure Sensors and Genetic Algorithm.

Author information

1
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China. lihk@dlut.edu.cn.
2
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China. strange@mail.dlut.edu.cn.
3
Laboratoire Vibrations Acoustique, University of Lyon, INSA-Lyon, LVA EA677, Villeurbanne F-69621, France. strange@mail.dlut.edu.cn.
4
Department of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria 0002, South Africa. reza.malekian@ieee.org.
5
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia. zhixiong.li@ieee.org.

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.

KEYWORDS:

centrifugal compressor; continuous wavelet transform; defect identification; genetic algorithm; pressure sensor; stochastic resonance; woods-saxon and Gaussian

PMID:
29671821
PMCID:
PMC5948608
DOI:
10.3390/s18041264
[Indexed for MEDLINE]
Free PMC Article

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