Abstract
In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.
Highlights
GWO-Variational Mode Decomposition (VMD) and VMD were both applied to the same experimental signal for decomposition, the correlation-based signal reconstruction proposed in Section 3.2 was applied to the two decomposition results to rebuild the signals; four performance indicators—Kurtosis, Signal–Noise Ratio (SNR), RMSE, and permutation entropy—of the two reconstructed signals were compared to verify the signal reconstruction effects
After the reconstructed signals were obtained through the decomposition by GWOVMD and VMD, respectively, 18 feature parameters were calculated on the 150 samples; two 18 × 150 feature matrices were obtained
Fault classification results by Differential Evolutionary (DE)-Kernel Extreme Learning Machine (KELM) on the feature matrices calculated through GWO-VMD and VMD are shown in Figures 20 and 21, respectively
Summary
Because of their large transmission ratio, high efficiency, and compactness [1] These types of machinery and equipment are generally operating in harsh environments, and the gearboxes are often operating in high-load states. Noise contained in gearboxes makes it difficult to distinguish between useful feature signals and interference in the fault feature extraction process. This problem could lead to misdiagnosis in the fault classification stage. Finding effective methods to pre-process signals and extract fault features from non-stationary vibration signals containing wideband noise, and classify the features accurately has become a key issue in gearbox fault diagnosis research
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