In general, deep learning-based fault diagnosis methods need a large number of training samples, which are often not available in real applications. Aiming at this problem, this article develops a new data augmentation method, i.e., randomized wavelet expansion (RWE), to generate a set of synthesis samples that share similar characteristics with the original sample. The first key point is that the amplitudes of wavelet coefficients at a randomly selected frequency band are enlarged through random expansion. Another key point is that the synthesis samples are processed to have the same mean values and standard deviations as their corresponding original sample. Afterward, the synthesis samples are used as the training dataset to train a deep convolutional neural network (CNN) for implementing the few-shot fault diagnosis of aviation hydraulic pumps. Finally, the performance has been validated through a series of experiments.

Aiming at the segmentation nonlinear degradation characteristics of IGBT, the traditional single remaining useful lifetime (RUL) method has low accuracy. This paper proposes a method combining gray prediction and particle filter algorithm. The gray prediction model is used for slow degradation trends prediction in the early stage. When the health precusor parameters reach the fault warning line, the improved particle filter algorithm is used for this stage’s prediction with the characteristics of fast nonlinear degradation. The comparison analysis result shows that the combinatorial prediction algorithms used in this paper can be better for tracking the degradation trends of IGBT, and the prediction accuracy is higher than either of the two single prediction methods.

Aluminum electrolytic capacitors (AECs) get multiple superior functions such as filtering, energy storage and decoupling, which have a great effect on the performance and lifetime for power converters. Therefore, analyzing and predicting the faults of Aluminum electrolytic capacitors (AECs) is conducive to improve the safety and reliability of the power converters. In order to establish the AECs’ fault prediction model and improve the accuracy, an integrated model based on complete ensemble empirical mode decomposition with adaptive noise, grey wolf optimization algorithm and regularized extreme learning machine (CEEMDAN-GWO-RELM) is proposed. The CEEMDAN is used to decompose the time series of AEC degradation process into several sequences, which can decouple the feature of local fluctuations from global degradation in the AEC time series. Then, the RELM optimized by GWO is used to predict each sequence after decomposition. RELM has the advantages of fewer hyperparameters and less operation time, and GWO with strong astringency is used for its optimization to obtain better fault prediction. Eventually, the predicted values are reconstructed to obtain the predicted values of the integrated model. The results show that, based on the aging data of AEC, the integrated model based on CEEMDAN-GWO-RELM can provide better prediction progress than traditional models, and the maximum relative error of each prediction time point is lower than 1.6%.

As a core component of automobile transmission, rolling bearings play a main role in the safety and reliability of vehicles. Existing diagnostic models often treat all features equally after feature extraction, without effectively distinguishing the importance of fault features, resulting in low accuracy and poor robustness in bearing fault diagnosis. To address this issue, a fault diagnosis model for bearings under multiple operating conditions based on feature parameterization weighting is proposed. The model utilizes a feature parameterization weighting module to categorize faults into two classes based on differences in means and implements different feature processing methods. The experimental results validate that the proposed feature parameterization weighting module effectively improves the diagnostic accuracy of the model by 8.95%. In terms of noise resistance, on two multi-condition datasets, the proposed diagnostic model achieves diagnostic accuracy of 98.79% and 98.36%. The diagnostic accuracy is improved by 15.7% and 22.48%, which indicates that the model has strong anti-noise ability.

To address the problems of poor model diagnosis and poor noise immunity caused by inconsistent distribution of bearing fault features and difficulty in feature extraction in multi-condition environments, a multi-condition bearing fault diagnosis method based on a channel segmentation improved residual network is proposed. A channel segmentation mechanism is designed for channel information highlighting, by selecting one channel of the three-channel feature image as the main operation channel, stacking it with the secondary operation channel after convolution, and then inputting the stacked feature map into the convolutional neural network to realize the extraction and classification of bearing fault features. Four different network models were selected to verify the diagnostic performance of the channel segmentation mechanism on the Case Western Reserve University bearing dataset and the Jiangnan University bearing dataset, and noise immunity experiments were conducted on the Jiangnan University bearing dataset. The experiments show that the proposed diagnostic model on the Case Western Reserve bearing dataset has a minimum improvement of 6.8% compared to the comparison method for multi-case bearing fault diagnosis experiments. In terms of noise immunity, the diagnostic accuracy of the fault diagnosis model with the addition of the channel cut-off mechanism improves the diagnostic accuracy of the noisy data by an average of 4.3% compared to that without the addition. The proposed model still has excellent diagnostic performance when diagnosing variable speed bearing faults.

For video streaming, Adaptive BitRate (ABR) algorithms are usually used to improve end-to-end user’s Quality of Experience (QoE). Many of the state-of-the-art ABR algorithms are based on simplified models, leading to conservative predictions of real situations. To optimize the QoE in complex end-to-end transmission environments, ABR algorithms based on Deep Reinforcement Learning (DRL) has shown a great improvement compared to traditional algorithms. However, the slow convergence of existing DRL-based ABR algorithms limits the QoE performance under dynamic video streaming environments. In this paper, we propose Fastconv, a novel DRL-based ABR algorithm that has a fast convergence speed to ensure a satisfactory QoE performance. Our work can be mainly divided into two parts. First, we preprocess the input data with large fluctuation in order to obtain the steady input and reduce the indeterminacy of convergence. Second, in order to reduce the structural complexity of the neural network itself and the number of parameters, we propose a neural network architecture based on multiplexed convolution kernel. Experiment results based on a real traced mobile dataset have demonstrated that Fastconv outperforms both the traditional and DRL-based ABR algorithms in terms of QoE.