Abstract
According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.
Highlights
Due to the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor valve vibration signals, using the traditional linear theory of signal analysis methods to fault diagnosis has more limitation and more difficult to effectively extract fault features
The traditional multi-fractal method is susceptible to the non-stationary trend of time series and cannot accurately reveal its multi-fractal characteristics. we found that the multi-fractal detrended fluctuation analysis (MF-DFA) method proposed by Kantelhardt et al [10], compares with the traditional multifractal method, it cannot only reflect the fractal characteristics of nonlinear signals as a whole, and accurately describe the local dynamic characteristics of vibration signals [11,12,13]
By observing the multi-fractal singular spectrum obtained by the MF-DFA method, it can be seen that the following important parameters can quantitatively express the probability distribution ratio and unevenness degree of fractal structure shown in Fig. 3: the spectral width ∆α = α − α describes the unevenness of probability measure distribution in the whole fractal structure
Summary
Nonstationarity and multi-component coupling characteristics of reciprocating compressor valve vibration signals, using the traditional linear theory of signal analysis methods to fault diagnosis has more limitation and more difficult to effectively extract fault features. We found that the multi-fractal detrended fluctuation analysis (MF-DFA) method proposed by Kantelhardt et al [10], compares with the traditional multifractal method, it cannot only reflect the fractal characteristics of nonlinear signals as a whole, and accurately describe the local dynamic characteristics of vibration signals [11,12,13]. It can reveal the multifractal features hidden in non-stationary time series and accurately estimate the multifractal spectrum by eliminating sequence trend terms by DFA [14].
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