In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

Various animals exhibit accurate navigation using environmental cues. The Earth’s magnetic field has been proven to be a reliable information source for long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for navigation and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during navigation. To mitigate the impact of geomagnetic anomalies, we develop a mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation (MLE). We integrate the developed mechanism with the TA-LSTM and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate the resilience of our navigation approach against geomagnetic anomalies, along with the precision and stability of underwater navigation in single and multiple destination missions.

In this study, we employ the active noise control (ANC) method to eliminate the low-frequency part of the noise generated by the rotation of the axial fan in heating, ventilation, and air-conditioning (HVAC) pipelines. Because the traditional variable step size least mean square (VSS-LMS) algorithm has poor tracking performance, we propose a variable step size filtered-X least mean square (FXLMS) algorithm based on the arctangent function to improve the adaptive filtering method of the convergence speed and noise cancellation effect. The step size of the proposed algorithm can be adjusted according to the error. When the error signal is significant, a larger step can be obtained, and when the error is small, the step size smoothness of the algorithm can be optimized. Compared with the traditional VSS-LMS algorithm, the convergence speed of the proposed algorithm is increased by 29%, the noise reduction effect is enhanced by 19%, and the mean square error (MSE) is reduced by 23% (0.0084). In addition, we developed a hardware experimental platform based on noise characteristics. In the noise reduction test using a GB/T 5836.2-06 standard PVC pipeline, the system reduced the noise by 12–17 dB.

Due to the absence of Global Navigation Satellite Systems (GNSS) in the underwater environment, navigation utilizing the Geophysical Magnetic Fields of the Earth (GMFE) emerges as a promising field. Geomagnetic gradient-based navigation systems rely on pre-mapping designated areas to minimize the effects of magnetic anomalies on navigation performance. However, obtaining magnetic maps beforehand is challenging, and relying solely on historical maps is insufficient for describing magnetic anomalies. Significant changes in magnetic field gradients caused by unidentified anomalies can render conventional geomagnetic navigation methods ineffective. In this article, we present a novel method called the Geomagnetic Gradient Multi-Attention Neural Network (GGMA). Our proposed encoder and decoder attention mechanism is capable of predicting the position and geomagnetic trends based on various geomagnetic gradient elements extracted from the trajectory data. We additionally utilize maximum likelihood estimation to ascertain the probability of the current coordinates being located within the anomalous region. By integrating the expected heading with the calculated heading, the effect of magnetic field anomalies on geomagnetic navigation is minimized. The GGMA method is effective in solving the navigation failure problem of magnetic field gradient-based navigation methods in the presence of magnetic field anomalies during long-distance simulation navigation experiments. It has higher navigation efficiency and accuracy compared to other algorithms in regions without anomalies.

Voltage sag state estimation on the basis of a limited number of installed monitors is essential to dividing the responsibility for the voltage sag and taking corresponding measurements for improvement in voltage quality. Therefore, a deep learning methodology via bidirectional WaveNet for the voltage sag state estimation is proposed in this paper. The presented method can simultaneously estimate voltage sag state at non-monitored buses via limited monitors. Especially, the proposed deep learning method using the bidirectional WaveNet is designed to explore the long-term and long-range temporal dependencies in both the forward and backward directions. In this way, only by using original measured voltages through monitors, high accuracy for voltage sag state estimation can be achieved without restructured or redesign of the raw monitored data. An excellent advantage of the presented algorithm is that it can be implemented without system parameters or operating conditions or any other prior information. The presented methodology was verified by the IEEE 30-bus benchmark system. The experimental results illustrated that the accuracy of the voltage sag state estimation results was over 99.83%. Furthermore, a comparison among different models, including the bidirectional GRU-based model, one-way WaveNet-based model, and bidirectional WaveNet-based model, was also conducted. The results illustrated that the proposed bidirectional WaveNet-based model achieved the highest accuracy and quickest convergence speed.

The advancement in autonomous vehicles has empowered navigation and exploration in unknown environments. Geomagnetic navigation for autonomous vehicles has drawn increasing attention with its independence from the global positioning system (GPS) or inertial navigation devices. While geomagnetic navigation approaches have been extensively investigated, the generalizability of learned geomagnetic navigation strategies remains unexplored. The performance of a learned strategy can degrade outside of its source domain where the strategy is learned, due to a lack of knowledge about the geomagnetic characteristics in newly entered areas. This article explores the generalization of learned geomagnetic navigation strategies via deep reinforcement learning (DRL). Particularly, we use DRL agents to learn multiple teacher models from distributed areas that represent dispersed navigation strategies and amalgamate the teacher models for generalizability across navigation areas. We design a reward shaping mechanism in training teacher models where we integrate both potential-based and intrinsic-motivated rewards. The designed reward shaping can enhance the exploration efficiency of the DRL agent and improve the representation of the teacher models. Upon the gained teacher models, we use multiteacher policy distillation to merge the policies learned by individual teachers, leading to a navigation strategy with generalizability across navigation areas. We conduct numerical simulations, and the results demonstrate an effective transfer of the learned DRL model from a source domain to new navigation areas. Compared with existing evolutionary-based geomagnetic navigation methods, our approach provides superior performance in terms of navigation length, duration, heading deviation, and success rate in cross-domain navigation.