Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

Hu, Bingbing; Li, Bing

doi:10.1155/2015/954932

Cited by 20 publications

(17 citation statements)

References 32 publications

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“…The non-stationary signals are divided into a great many of small segments, and each small segment can be regarded as a stationary or cyclostationary signal. Therefore, the traditional spectrum analysis technology still has important research significance and a very wide range of applications in the rotating machinery fault diagnosis for its simplicity and intuitive [13][14][15]. The most representative spectral analysis technologies are holospectrum technology [16][17][18], full-spectrum technology [19] and vector spectrum technology [20,21].…”

Section: Introductionmentioning

confidence: 99%

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

Gu¹,

Zhu²,

Zeng³

et al. 2019

J. vibroeng.

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As an important characteristic information in incipient fault diagnosis of rotating machinery, the fault impulse signal is hard to be monitored due to the low signal amplitude and system disturbance/noise. Based on bilateral spectrum and precession energy difference density spectrum for the incipient fault diagnosis of rotating machinery, a novel diagnosis method is proposed in this paper to overcome this key problem. Compared with the existing methods to extract transient impulses from the vibrate signals, this paper designs a new fault feature parameter-precession energy difference density to characterize the feature of transient impulse. Furthermore, the complex signal and the negative frequency are introduced into the spectrum analysis and the forward and backward precession characteristics, which can be directly gained through the bilateral spectrum and relieves the problems not to be overlooked, such as high calculation, high error and time consuming. Finally, the feasibility and effectiveness of the proposed methods are demonstrated via a case study of a vertical mill reducer.

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Section: Introductionmentioning

confidence: 99%

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

Gu¹,

Zhu²,

Zeng³

et al. 2019

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Qin et al, found the frequency range selection characteristic of re-scaling frequency stochastic resonance because of the driving frequency limitation of bistable stochastic resonance and then separated vibration components with different frequencies by iteratively using stochastic resonance [ 21 ]. Hu and Li applied an adaptive stochastic resonance method to diagnose crack defect [ 22 ]. However, there is seldom reference in the literature about the centrifugal compressor blade crack detection by using stochastic resonance methods.…”

Section: Introductionmentioning

confidence: 99%

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

Malekian

et al. 2018

Sensors

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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.

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“…In severe cases when rolling bearings early break down during serving the machines, the machines can be damaged. In fact, the unpredictable failures may cause serious damage to machinery and equipment, so the extraction of early defects is very important to ensure the reliable operation of the machinery [3]. It is difficult for the vibration signal of the rolling bearing which is nonstationary and strongly modulated to detect weak fault characteristics under strong background noise [4].…”

Section: Introductionmentioning

confidence: 99%

Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

Shi

2016

Shock and Vibration

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A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

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Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

Cited by 20 publications

References 32 publications

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

An incipient fault diagnosis method for rotating machinery based on bilateral spectrum and precession energy difference density spectrum

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

Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

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