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.
Highlights
During the operation of a centrifugal compressor, the blades suffer from combined effects of centrifugal force, unsteady flow and vibration and so forth [1]
In the condition that the consistent defect characteristic frequency can be extracted from both strain signal and pressure pulsation signal with the proposed method, it can be verified this method is effective and has potential in the application of long-term condition monitoring and weak defect warning for large-scale centrifugal compressor blades
Condition monitoring and incipient weak defect warning for centrifugal compressor blades is always a big challenge as the impeller works in a closed space, which means the signal that can effectively reflect the blade’s state is difficult to acquire
Summary
During the operation of a centrifugal compressor, the blades suffer from combined effects of centrifugal force, unsteady flow and vibration and so forth [1]. The stochastic resonance method utilizing noise reasonably can be considered for the weak defect characteristic frequency enhancement of the cracked blade. Many scholars found that the stochastic resonance is conducive to extract weak characteristics from strong noise and this nonlinear phenomenon has been widely used in the identification of early weak fault [13,14,15,16,17,18]. Wherein, He et al, studied the multi-scale noise tuning methods [19].
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