Abstract
Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).
Highlights
As everyone knows, most mechanical fault signals have the characteristics of being nonlinear and non-stationary; it is difficult to accurately reveal the fault feature frequency applying directly fastFourier transform (FFT) in data processing [1]
Novelties and attractions in the article are that a novel time-frequency analysis (TFA) technique, named the improved adaptive variational mode decomposition (IAVMD), is designed for rotor fault diagnosis, which can automatically separate a non-stationary compound signal into several intrinsic mode function (IMF) ingredients and achieve directly time-frequency contents
To further verify the presented IAVMD method, we process the experimental data collected from the rotor-bearing system in the vibration measurement laboratory at North China Electric
Summary
Most mechanical fault signals have the characteristics of being nonlinear and non-stationary; it is difficult to accurately reveal the fault feature frequency applying directly fast. Dragomiretskiy and Zosso [14] presented an alternative TFA tool termed variational mode decomposition (VMD), which can decompose a complex and irregular compound signal into several sub-signals named intrinsic mode function (IMF). Two appropriate parameters (i.e., the penalty factor αand mode number K) need to be selected in advance when we apply VMD in fault detection. Novelties and attractions in the article are that a novel TFA technique, named the improved adaptive variational mode decomposition (IAVMD), is designed for rotor fault diagnosis, which can automatically separate a non-stationary compound signal into several IMF ingredients and achieve directly time-frequency contents. Additional details of VMD can be found in the original article [14]
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