Aluminum electrolytic capacitor (AEC) is one of the most age-affected components in power electronic converters and its reliability has been a major concern. As the growth of service life, the equivalent series resistance (ESR) of AEC increases due to the loss of electrolyte. Therefore, online ESR estimation is critical for condition monitoring of AEC. This paper proposes an online ESR estimation method for output capacitor of boost converter. By analyzing the voltage ripple, ESR calculation model is derived. Based on the model, only output voltage and inductor current need to be measured, which can be obtained directly from double closed-loops converter. To implement this proposed method, a detailed extraction scheme based on wavelet transform denoising (WTD) is presented. Non-ideal factors (noise and voltage spikes) are analyzed and the superiority of the proposed parameter identification scheme is verified. The proposed estimation method can obtain ESR for the converter operating at different working conditions such as resistive loads and converter loads. Effect of temperature on ESR is incorporated for accurate determination of the health of capacitor. Simulation and experimental results are provided to verify the effectiveness of the method.

The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability. Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.

A new topology of five‐phase fault‐tolerant doubly salient electromagnetic generator (FTDSEG) is proposed for high reliability applications in this paper. The analysis of no‐load and loading operations shows the potential fault‐tolerance capability. On the basis of comparative analysis on structure characteristics between five‐phase non‐FTDSEG (NFTDSEG) and five‐phase FTDSEG, the variation of rotor pole number from NFTDSEG to FTDSEG is revealed. Meanwhile, the flux linkage and electromotive forces (EMFs) of five‐phase NFTDSEG and five‐phase FTDSEG are investigated with finite element method and five faults of five‐phase FTDSEG are analysed in detail. Especially when single phase short‐circuit fault happens, the magnetic field of the motor, flux linkage of short‐circuit phase and short‐circuit currents without load are investigated in four different short‐circuit moments and the fault is in consideration of armature winding resistance. The ultimate variation trend of flux linkage is irrelevant to moment when the short‐circuit fault happens. The flux linkage and EMF of short‐circuit phase are also analysed. The laws of fault operating influence on external and power characteristics of FTDSEG are researched in five faults with different exciting currents. The fault‐tolerant ability of five‐phase FTDSEG has been demonstrated by the experimental results.

The wound field doubly salient generator (WFDSG) are receiving consideration for the field of industrial and aerospace/aviation, since it has the advantages of high robustness with simple structure and adjustable output voltage. In this paper, upon the analysis of generator structures, the features of three rectifiers which are suitable for WFDSG are analyzed in detail. The comparative research of WFDSG with hybrid half-bridge rectifier (HHBR), full-bridge rectifier (FBR) and half-bridge rectifier (HBR) is presented by the finite-element method (FEM). Then, the load and no-load characteristics of WFDSG under the three rectifiers mentioned above are researched comparatively. The commutation overlap angles and total commutation output voltage losses of three rectifiers are deduced. The experiments of a 12/8-pole single armature-winding WFDSG (SAW-WFDSG) with FBR and a 12/8-pole dual armature-winding WFDSG (DAW-WFDSG) with HHBR prototypes have been performed. The FEM and experiment results shows that the SAW-WFDSG with FBR and the DAW-WFDSG with HHBR both have a good application prospect.

Imbalanced data present a significant challenge in intelligent fault diagnosis due to limited sample availability, various generative models have been proposed for data augmentation. However, these training processes are often complex, prone to model collapse, and fail to improve the classifier’s ability to categorize fault signals. Consequently, a bearing fault diagnosis method based on condition denoising diffusion probabilistic model (DDPM) and improved convolution neural network(CNN) is proposed to address class-imbalanced. Firstly, the conditon DDPM is introduced to generate high-quality and diverse vibration synthetic data through accurate physical simulation. Secondly, the synthetic data’s effectiveness is assessed using the proposed improved CNN, which offers enhanced flexibility in perception. Thirdly, the GAN-train/GAN-test is used to provide a comprehensive evaluation method for synthetic data and indicate the diversity and similarity of synthetic sample. Finally, two machinery datasets, five classifier models and seven data augmentation methods are compared under varying imbalance ratios of 1:2, 1:4, and 1:10. These results confirm the sample generation capabilities of condition DDPM and the high classification performance of improved CNN.