This paper focuses on the application of digital twins in the field of electric motor fault diagnosis. Firstly, it explains the origin, concept, key technology and application areas of digital twins, compares and analyzes the advantages and disadvantages of digital twin technology and traditional methods in the application of electric motor fault diagnosis, discusses in depth the key technology of digital twins in electric motor fault diagnosis, including data acquisition and processing, digital modeling, data analysis and mining, visualization technology, etc., and enumerates digital twin application examples in the fields of induction motors, permanent magnet synchronous motors, wind turbines and other motor fields. A concept of multi-phase synchronous generator fault diagnosis based on digital twins is given, and challenges and future development directions are discussed.

The medium voltage DC integrated power system, as the heart of large ships, relies on accurate state acquisition for safe and stable operation. Traditional measurement methods are not sufficient to meet the requirements for accurate data acquisition in complex environments. Extended Kalman filtering is an estimation method that can accurately estimate the current state of the system and predict the state of the system at the next moment. Based on the typical network structure of the medium voltage DC integrated power system on ships, this paper establishes a state space mathematical model of the system, proposes an extended Kalman filtering method for the system under sudden load changes, and achieves state estimation and prediction of the system, enhancing the precision of data acquisition. The effectiveness of the model and algorithm is verified through simulation.

To address non-complete data issues such as missing, distorted, redundant and chaotic data in distribution network condition monitoring data easily caused by ship faults (e.g., sensor failure, cable short circuit, etc.), an improved optimization algorithm (IPS-SH5N1) considering the demand for incomplete information reconstruction in the distribution network of ship integrated power systems is proposed based on the SH5N1 meta-heuristic optimization algorithm. This algorithm is used to optimize the weights and thresholds of a BPNN, achieving accurate completion of non-complete information in the distribution network. The IPS-SH5N1-BPNN is applied to conduct information reconstruction verification on 2000 groups of incomplete data samples of ship integrated power systems. The results show that the information reconstruction accuracy (MSE) of this method is improved by more than 90% compared with the BPNN; the model training convergence time and global optimization time consumption are significantly shortened, and the average online single information reconstruction time is reduced to 4.45 ms. This method has core advantages of high precision, fast convergence, strong robustness and excellent real-time performance, which can provide reliable technical support for the intelligent operation and maintenance of ship integrated power systems.

When a fault occurs in a shipboard integrated power system, traditional reconfiguration strategies only adopt simple switching operations to change the system topology so as to isolate the faulty area. However, such strategies have limitations. For instance, under some specific operating conditions, the propulsion load is not allowed to lose power completely, and certain critical loads cannot tolerate power interruption. Considering that the propulsion load is a high-power load compared with other loads in the shipboard integrated power system, the navigation speed can be reduced according to the relationship between speed and power in the reconfiguration strategy to ensure the power supply of other loads. In addition, traditional optimization methods also suffer from drawbacks, such as being prone to local optima or failing to solve the fault reconfiguration problem of the shipboard integrated power system in real time. Therefore, this paper uses the DQN method to simulate and verify the proposed objective function under cruise operating conditions. The results demonstrate the effectiveness and real-time performance of the proposed method.

As a key technology for wireless signal identification, modulation recognition plays an important role in the fields of unmanned aerial vehicle (UAV) communications, low-altitude spectrum management, etc. However, the accuracy of modulation recognition often cannot be guaranteed in scenarios with serious noise interference when a few samples are available. In this paper, we propose an intelligent modulation recognition method for UAV signals based on small-sample augmentation and soft threshold denoising. We first propose a new dual-driven dataset expansion method by combining the UAV air–ground channel propagation model with the received data samples. Then, we construct a background learning-based long short-term memory (BL-LSTM) model to extract the environmental background features embedded in the UAV signal, including Line-of-Sight (LoS) state, multi-scale fading parameters and Doppler shift characteristics. We integrate environmental background information into the data training model and optimize the authenticity of data distribution. As a result, the model adaptability can be enhanced. Finally, we construct a deep residual shrinkage network based on the soft threshold function (STF-DRSN). By leveraging the capability of the soft threshold that resists noise interference, we integrate it into each residual block of the deep residual shrinkage network. Simulation results show that compared with the state of the art, our method can improve the modulation recognition accuracy of UAV signals in small-sample scenarios.