The health management of railway vehicles is crucial to secure safety and efficiency in the long-term operation of high-speed trains. Meanwhile, complex components put forward a higher requirement for the robustness of condition monitoring systems, especially abilities to identify unexpected faults. The misidentification of infrequent faults could lead to unpredictable consequences to the vehicle's safety. This paper proposes a novel method for detecting unexpected faults of high-speed train bogie based on Bayesian deep learning. First, a Monte Carlo-Based perturbation is imposed on input samples, which can magnify the difference between unexpected faults and known ones. Then, through dropout-based Bayesian deep learning, the diagnosis result can be obtained as well as a Bayesian indicator of whether the anomalies belong to known classes. The proposed method can capture the uncertainty of model outputs and identify unexpected faults, with only a few samples of unexpected anomalies for calibration. Also, it is compatible with most existing neural network structures. The experiments compare the proposed method with existing methods on two real-world applications, which demonstrates the effectiveness and superiority of the proposed scheme.

Due to the volatility of natural resources, the power fluctuations of photovoltaic (PV) generation have serious negative impacts on the power quality. This paper reports a novel approach to resolve this problem in a combined cascade hydro-PV-pumped storage (CH-PV-PS) generation system through appropriate power distribution on a small time scale. According to the characteristics of power generation systems and multi-constraints, how to obtain the optimal smoothing effects with the small cost is a key challenge. For this purpose, wavelet packet decomposition is modified by constructing cost function and ideal power trajectory sequences to obtain a new adaptive power distribution method for the CH-PV-PS generation system considering the boundary conditions of the system in this paper. At the same time, to meet the real-time control requirements of actual systems, an additive function is presented to improve the optimization speed of the adaptive power distribution method. In the meantime, a fuzzy controller is designed to optimize the inevitable delay time of the power scheduling system, and the starting threshold is set to avoid the frequently mode conversion of pumped storage. Finally, the performance of the method is evaluated based on the PV station data in Xiaojin County, Sichuan Province, China. Simulation results based on Matlab-simulink indicate that the proposed method can effectively suppress PV power fluctuations and ensure normal operation of the CH-PV-PS generation system within the multiple constraints.

High-speed train bogies are essential for the safety and comfort of train operation. The performance of the bogie usually degrades before it fails, so it is necessary to detect the performance degradation of a high-speed train bogie in advance. In this paper, with two key dampers on the bogie taken as experimental objects (lateral damper and yaw damper), a novel 1D-ConvLSTM time-distributed convolutional neural network (CLTD-CNN) is proposed to estimate the performance degradation of a high-speed train bogie. The proposed CLTD-CNN is an encoder-decoder structure. Specifically, the encoder part of the proposed structure consists of a time-distributed 1D-CNN module and a 1D-ConvLSTM. The decoder part consists of a 1D-ConvLSTM and a simple time-CNN with residual connections. In addition, an auxiliary training part is introduced into the structure to support CLTD-CNN in learning the performance degradation trend characteristic, and a special input format is designed for this structure. The whole structure is end-to-end and does not require expert knowledge or engineering experience. The effectiveness of the proposed CLTD-CNN is tested by the high-speed train CRH380A under different performance states. The experimental results demonstrate the superiority of CLTD-CNN. Compared to other methods, the estimation error of CLTD-CNN is the smallest.

The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.

This paper proposed a new electrical synaptic transmission-based Spiking Neural P system (SNP system) based on SNP systems. Some new elements are added into the original definition of SNP systems, such as new synapses, bidirectional model, two types of neurons and cancelling the delay of axon. Because SNP system is easy to express the logical relationship between graphics and has strong ability to process information in parallel, the bidirectional characteristics of electrical synaptic transmission is effectively combined with the electrical quantity (direction of current) for fault location of distribution network with distributed generations (DGs) in this paper. The fault location model and reasoning algorithm of the electrical synaptic transmission-based SNP system are studied with the advantages of high accuracy, less computation, simple and intuitive model and reasoning. Furthermore, the algorithm is applied reasonably in the bidirectional power flow characteristics of a distribution network with DGs. Finally, this paper verifies the effectiveness, accuracy and reliability of the method through two cases which involve the single fault, multiple fault and misinformation fault. With the large-scale DGs (distributed generation) access to the distribution network, which changes the power flow structure and operation mode, the traditional protection strategy of the passive distribution network is no longer applicable. The fault location problem of distribution network with DGs is carried out. Firstly, Many fault location methods in the existing literatures have been anaylsised in this paper, then the electrical synaptic transmission-based spiking neural P system (ESSNP) has been proposed to solve the fault location problem. Foremore, the fault diagnosis mode for the power outage section with ESSNP has been established, which construct with Network description matrix and Fault current matrix. In order to verify the accuracy of the method in this paper, three cases have been studied, which the single fault, multiple faults and misinformation faults in each case. Finally, conclusions have been discussed.