Local mean decomposition (LMD) is a self-adaptive method, which has been widely applied to extract early fault signals from bearings. However, mode mixing occurs during the decomposition process. Moreover, in processing signals with strong noise, false frequency components can be generated by variational mode decomposition (VMD). To address these problems, a weak fault extraction method based on VMD is proposed for rolling bearings. This method regards LMD and the combination production function (CPF) as prefilters for VMD. First, LMD is used for denoising the original signal, and then the CPF components that contain the fault information are combined into a new signal. Second, this method determines the decomposition level K of the VMD from the spectral peaks of the recombined signal. Finally, this method decomposes the recombined signal using the VMD. The main contributions of the proposed method are (i) the CPF method is employed for adaptively de-noising, and the power of the fault feature can be improved; (ii) the decomposition level K of the VMD can be determined adaptively. After processing a simulated signal, fault information of the gears and rolling elements is successfully extracted, thereby demonstrating the feasibility of the presented method. Moreover, the feasibility of the proposed method is further demonstrated in a comparison of results with those obtained from the MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) algorithm.

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

In this paper, a new feature extraction method called refined composite multiscale global fuzzy entropy (RCMGFE) is proposed. Based on the proposed RCMGFE and self‐organizing fuzzy logic classifier (SOF), a new method for bearing fault diagnosis is proposed. Firstly, the fault features of the original bearing signal are extracted by using the proposed refined composite multiscale global fuzzy entropy, and the fault feature set of RCMGFE is constructed on this basis. Secondly, the extracted RCMGFE fault feature set is divided into an offline training sample set, an online training sample set, and a testing sample set. The offline training sample set and the online training sample set are, respectively, input into the offline training stage and the online training stage of the SOF for selecting representative samples and constructing fuzzy rules. Then, the testing sample set is input to the testing stage of the SOF for classification. Finally, the data of drive end bearing and fan end bearing provided by Case Western Reserve University are used to verify the validity of the proposed fault diagnosis method. The experimental results show that, compared with other methods, the proposed fault diagnosis method has a higher classification effect.

The adaptive chirp mode decomposition method has a good effect on processing chirp signals. The parameter α controls the smoothness of the output signal. Too small an α will cause a smooth output signal. The parameter β controls the instantaneous frequency (IF). If too small a β value is used, the output IF will be very smooth. However, rapidly changing IFs require a relatively large β. However, the choice of α,β is artificially set, and there are errors in practical applications. Therefore, it employs the state transition algorithm to adaptively optimize α,β to improve the signal-to-noise ratio (SNR) and resolution of the signal. First, as the species number of the state transition algorithm method is set artificially and has a long running time, this paper proposes a Rastrigin optimization test equation to test the optimization time of different species and determine the number of optimal species; second, the state transition algorithm determined by the number of species is employed to adaptively find the α,β in the adaptive chirp mode decomposition algorithm; finally, the optimized adaptive chirp mode decomposition method is applied to the simulation signal and chirp signal from marine animals to verify the proposed method.

Style transfer has received much attention, and Chinese character style transfer is an important application. In the previous Chinese character style transfer methods, most of the methods separately establish a mapping from source font to target Chinese character for each specific target font. It is impossible to extend a single model to other styles. The model training is based on the amount of Chinese character data from a single font, and the demand of the character is greater. In fact, different font style transfer tasks have certain commonalities. Therefore, we use multiple fonts in the data set and combine these fonts into multiple sets of Chinese character style transfer tasks. These tasks will be trained at the same time, instead of that only one font style transfer task training is achieved at a time like the previous work. In order to achieve the purpose of extracting and fusing the content features and style feature, we propose a style transfer network composed of content encoder, style encoder, feature fusion module, and decoder, based on the fusion of multi-scale content and style features. Through the experimental results, it can be found that, using our method, training the style transfer of multiple fonts at the same time can help any one of them to generate better results with only a few samples.