Gear fault vibration signals are commonly non-stationary, and useful fault information is often buried in heavy noise, which makes it difficult to extract gear fault features. How to select the suitable fault frequency bands is the key to gear fault diagnosis. To address the above problems, a method combining the improved minimum entropy deconvolution (MED) and accugram, named IMEDA, is proposed for extracting gear fault features. Firstly, a selection index based on permutation entropy (PE) and correlation coefficient is defined. Then, the optimal filter length can be effectively selected by the step-length searching method using the proposed index as objective function, and the improved MED is employed to preprocess the gear vibration signals. Finally, the accugram analysis is performed for the preprocessed signals to obtain the optimal frequency band, and the fault characteristic frequencies are extracted from the square envelope spectrum of the signals in the optimal band. The method is validated by gear experimental data with gear wear-out failure. The analysis results demonstrate that the proposed method owns superior effect by comparing with the fast kurtogram (FK), MED combined with FK (MED-FK), accugram and infogram.
Maximum second-order cyclostationary blind deconvolution (CYCBD) is a good signal denoising method, which can be employed for gear fault diagnosis. However, CYCBD is highly dependent on the pre-period of the measured signal, which needs to be appropriately predetermined. To address
this issue, a new method based on the multi-point kurtosis (MKurt) spectrum and CYCBD is proposed, which can be used for extracting fault features in a gearbox. First, deconvolution period T, the key parameter of CYCBD, is accurately selected using the MKurt spectrum. Then, according to the
selected key parameter, CYCBD is employed to process the gearbox signals and identify the fault type. This proposed method is applied to the analysis of single and compound fault signals of a gearbox; the large gear and pinion fault signals under strong background noise are separated. Finally,
the fault signals obtained by CYCBD are analysed using an envelope spectrum to extract the fault features. The two case studies demonstrate that the proposed method can effectively identify the gear faults. Moreover, the results show the superior effectiveness and reliability of this proposed
method compared with the maximum correlation kurtosis deconvolution (MCKD) method.
The penalty factors and number of modes in variational mode decomposition (VMD) have to be set empirically. In addition, it is a challenge to select the most useful mode from the multiple mode components decomposed using this method. To solve these problems, an effective gear fault
diagnosis method using variational mode extraction (VME) and envelope analysis is proposed. Due to the blindness in determining the centre frequency of the desired mode, this study proposes a method to select the centre frequency based on the maximum signal-to-noise index among the meshing
frequency and its harmonics. The desired mode obtained according to the selected centre frequency has the maximum signal energy and relatively minimum noise energy. Two case studies verify that the presented approach is effective for gear fault feature extraction. Comparisons with VMD and
empirical mode decomposition (EMD) further highlight the superiority of the method.
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