Fault Diagnosis Based on Nonlinear Complexity Measurefor Reciprocating Compressor

Tang, Youfu

doi:10.3901/jme.2012.03.102

Cited by 8 publications

(5 citation statements)

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“…Therefore, it is also very important to analyze the nonlinear characteristics of mechanical vibration signals. The main methods of signal nonlinear analysis include the Pseudo-Poincare mapping diagrams that are based on phase space reconstruction theory, 29 fractal, 30 Lempel-Ziv complexity 31,32 and other methods to describe the characteristics of sophisticated system behavior. Complexity reflects velocity of emergence of new patterns as the length of the series increases, which can quantitatively describe the state changes of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

Li,

Wang,

Yang

et al. 2023

The International Journal of Acoustics and Vibration

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An air flow-driven micro-turbine, widely used in air-condition control systems in aircraft cabins, train coaches, etc., exhibits complex vibration behaviors under stable or unstable inlet flow conditions, and especially has a certain correlation with speeds. In this paper, the vibration responses of the micro-turbine undergoing stable and unstable inlet flows measured on a test rig are analyzed and compared by using different signal processing methods, which include time and frequency domain methods, and statistical and nonlinear methods. First, the test rig system of the airflow-driven micro-turbine, the instrument system and four typical experimental cases are introduced. Then the measured vibration signals are analyzed and compared by time domain characteristic parameters (peak-to-peak value, RMS value and kurtosis value), statistical parameters (auto-correlation and BDS), and amplitude spectra in the frequency domain, statistic spectrum indicators (SSI) based on Welch's periodogram of power spectra, and the spectra of selected IMF components based on Hilbert-Huang Transform (HHT). In particular, some nonlinear feature analyzing methods, including Pseudo-Poincare mapping diagrams and Lempel-Ziv(LZ) complexity, are also used for analyzing measured vibration responses. The obtained results using the above multiple methods are compared and show that, when the inlet flow of the turbine fluctuates significantly, the nonlinear characteristics of the turbine bearings are significantly higher than those of the relatively stable inlet flow and speed conditions. Under these circumstances, commonly used time-frequency analysis methods cannot characterize the different speed operating state of the turbine, and LZ-Complexity and other nonlinear characterization methods should be used to better understand the characteristics of different speeds under unstable conditions.This study provides references for the aerodynamic stability monitoring of the micro-turbine and its design improvement.

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Section: Introductionmentioning

confidence: 99%

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

Li,

Wang,

Yang

et al. 2023

The International Journal of Acoustics and Vibration

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show abstract

“…Commonly, fault diagnosis involves two steps: feature extraction and fault decision. The fault feature is extracted from the raw signals, and then the fault diagnosis is performed by manual experience judgment or pattern recognition technology [1][2][3][4][5][6][7][8][9][10][11][12][13]. Common feature extraction techniques include time domain statistical analysis, Fourier spectrum analysis, wavelet transform, empirical mode decomposition (EMD), and other time-frequency analysis methods [1,2,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…A small fault in reciprocating compressor may cause serious issues in operation. Its fault diagnosis also uses traditional method-feature extraction adding pattern recognition [6][7][8][9][10][11][12]. Liu Shulin et al [6] used wavelet packet energy of difficult frequency ranges and radial basis function (RBF) neural network to diagnose faults.…”

Section: Introductionmentioning

confidence: 99%

“…Liu Shulin et al [6] used wavelet packet energy of difficult frequency ranges and radial basis function (RBF) neural network to diagnose faults. Tang Youfu et al [7] calculated the Lempel-Ziv complexity indexes and used back-propagation (BP) neural network to identify the failure of the reciprocating compressor valve. A fault diagnosis method for bearing fault of reciprocating compressor based on LMD multiscale entropy and SVM is proposed [8].…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

Yang

Jiang-an

Chen

et al. 2019

Mathematical Problems in Engineering

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Reciprocating compressors are widely used in petroleum industry. Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals. This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN). The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification. The gas valve signals of reciprocating compressor in different states are used as the experimental data. In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy. In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing. The results show that the MSRCNN model has higher fault recognition rate than the traditional methods. This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.

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“…As the most common and vulnerable support components of rotating machinery, rolling bearings have become the major monitoring objects. erefore, feature extraction of bearing signals is a decisive factor for intelligent monitoring and diagnosis at present [1,2]. e dynamic parameters, such as the driving force, damping force, and elastic force of the mechanical system, always demonstrate the nonlinear variation signals, especially during the emergency stage of equipment failure.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

2019

Shock and Vibration

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According to the performance degradation problem of feature extraction from higher-order statistics in the context of alpha-stable noise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is decomposed into several product functions by LMD to realize signal stability. Then, the distribution properties of product functions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional lower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a fault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and intuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the presented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order feature extraction.

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Fault Diagnosis Based on Nonlinear Complexity Measurefor Reciprocating Compressor

Cited by 8 publications

References 0 publications

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

Research on Micro-Turbine Operating State Characterization Based on Bearing Vibration Signals Analysis

Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

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