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• The GS-TQWT UGW defect features extraction model is proposed. • A simulation signal conforming to the characteristics of UGW is constructed. • The adaptive parameters selection of the GS-TQWT model is completed. • The non-convex penalty function is used for optimization. Ultrasonic guided wave （UGW） is suitable for defect detection of long weld, but it is difficult to extract defect echo features due to dispersion, multi-mode, background noise and structural noise. To solve this problem, a group sparse tunable Q-factor wavelet transform (GS-TQWT) model is proposed in this paper. Firstly, it is revealed that the defect echo of UGW nondestructive testing (NDT) has the characteristic of group sparsity. Based on this, the UGW defect features extraction model of the GS-TQWT is established. Then, a simulation signal is constructed according to the dispersion and attenuation characteristics of UGW, and the adaptive selection of the GS-TQWT optimal parameters has been completed based on the simulation signal. Moreover, the majorization-minimization (MM) algorithm is used to solve the model. Finally, the experiment of UGW weld defect detection was carried out to verify the effectiveness of the GS-TQWT model. [ABSTRACT FROM AUTHOR]

• Extracting weak fault features from complex bearing signals. • Diagnosing the fault of the bearing according to the impulse and periodicity. • Improving the running speed of dictionary learning algorithm. Generally, the transient characteristics of early bearing failure are not obvious. How to extract weak transient features is a big challenge. Dictionary learning has been successfully used to extract bearing fault features. However, the traditional dictionary learning is easy to fall into local optimum and cannot extract fault features from complex signals. And it often consumes huge computational costs. In order to solve the above problems, this paper proposes a fault pulse extraction and feature enhancement method for bearing fault diagnosis. Firstly, the bearing vibration signal is segmented in the time domain. Then this paper proposes a multi-scale alternating direction multiplier method for dictionary learning (MADMMDL) to extract fault impact signal from the segment signal. Finally, frequency spectrum averaging is used to enhance the bearing fault characteristic frequency. Through numerical simulation and rail transit transmission failure simulation experimental analysis, the feasibility of this method in bearing fault diagnosis is verified. [ABSTRACT FROM AUTHOR]

• A feature extraction model of the converter transformer based on the vibration signal is proposed. • Markov transition field and continuous wavelet transform are used to transform time series into two-dimensional images. • Channel attention and spatial attention mechanism are introduced to fuse the feature tensors. • The feature image data set is established according to the measured vibration signal of the converter transformer. The vibration signal feature extraction model of the converter transformer has the problem of low accuracy. The main reason is that the time-related information is easily lost during training. In recent years, convolutional neural networks (CNN) have been proven to break the limits of traditional classification models. CNN has achieved remarkable results in large-scale image recognition tasks. To solve the problem of difficulty in model building and loss of time-related information, this paper proposes a multi-scale fusion feature extraction model for converter transformer vibration signal. The model includes an image generation module and a multi-scale information fusion module. The image generation module converts vibration signals into time-domain, frequency-domain, and energy feature images by calculating the Markov Transform Field and Continuous Wavelet Transform. In the multi-scale information fusion module, the attention module is introduced into the multi-parallel convolutional neural network to fuse the feature images. The proposed method can make full use of the advantages of deep learning through generating images that can represent time series from multi-scale. To evaluate the effectiveness of the model in this paper, we establish a data set based on the measured vibration signals and tested by classification experiments. The result shows the accuracy of the model state recognition in this paper is 96.15%, which is better than classic time series processing networks such as LSTM and 1D-CNN. [ABSTRACT FROM AUTHOR]

• Minimum entropy deconvolution is introduced into multiscale morphological filtering. • The influence of random impulses is eliminated. • Maximum scale of structure element in MMF can be determined adaptively. • Diagonal slice spectrum is introduced to choose the optimal morphological filter. The bearing fault signal can be seen as convolution of periodical impulses and interference components. The minimum entropy deconvolution (MED) is effective approach for the deconvolution of signal. However, the MED is vulnerable to random impulse and interference components. To solve the problem, an improved method, named minimum entropy morphological deconvolution (MEMD), is proposed in this paper. Firstly, the amplitude frequency response of two typical morphological operators (MOs) are discussed. These operators are then introduced into MED to filter the sample matrix. The optimal MO is selected based on the amplitude ratio of diagonal slice spectrum (DSS). Eventually, the filtered result is analyzed by DSS to identify the fault type. In MEMD, the influence of random shocks is eliminated and the scale of SE can be determined adaptively. The MEMD is verified by simulation and experimental signals. Comparison study is implemented and the analysis results verify its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

• High diagnostic accuracy be obtained with smaller data sets. • Without any manual time-consuming feature extraction process. • A strong self-adaptive ability, and suitable for practical application. • Using the dynamic routing algorithm in fault diagnosis of rotating machinery. The bearings fault diagnosis is essential for the maintenance and reliability of rotating machinery. Bearings pitting is one of the most common fault types of rotating machinery. However, due to the complex working conditions of bearings, it is challenging to diagnose the pitting faults in bearings inner and outer rings at different speeds. In this paper, an improved one-dimensional inception capsule network (IICN) is proposed to solve the problem of bearing pitting fault diagnosis under complex working conditions. Firstly, the raw bearing vibration signal is processed using the improved Inception network. The function of the stage is to approximate an optimal local sparse structure with a simple dense substructure for bearing healthy state feature extraction. And then inputs concatenated features to the primary capsule layer and the routing capsule layer. The inputs are mapped to feature vector space and weighted by the dynamic routing algorithm. The dynamic routing algorithm encodes the significant spatial relationship between low-level features and upper-level features. The Euclidean length of each capsule vector is the probability of belonging to this bearing healthy condition. In order to validate the effectiveness of the IICN method, bearings pitting experiments at different speeds were designed. The raw bearings vibration signal data under six different health conditions are collected, and the effectiveness of the IICN method is verified. Experimental results show that the IICN method can effectively distinguish different degrees of bearing pitting fault at different speeds, and its diagnostic accuracy is superior to other advanced deep learning methods. [ABSTRACT FROM AUTHOR]