Intelligent traffic systems for railway object detection have become the focus of research in recent years. Accurate and fast object detection using a camera is an important but challenging problem in the railway industry. In this study, we propose an object detector with low power yet robust detection for collision warning in a train safety system. The proposed object detector comprises three modules. First, a stable sampling module is used to reduce the dimensions of the feature map and image information loss. Second, a lightweight feature extraction module utilizes a dynamic bottleneck structure to control the calculation load and enhance the expressive ability of the model. Third, a feature-fusion module combines high- and low-level features to enhance the semantic information and improve the accuracy of detection of multiscale and small objects. Experimental results demonstrate that the proposed network achieves reasonable results for railway object detection and outperforms the current state-of-the-art detectors. Finally, we design an obstacle-avoidance device that can be installed at the front of the train for real-time security warning in real-world conditions.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of the fault identification of rolling bearings, a fault-detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique’s principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

The chain drive system represents a critical subsystem within the scraper conveyor. This paper proposes a joint simulation model for the drive system of the scraper conveyor, driven by the permanent magnet synchronous motor, in order to conduct a comprehensive analysis of the dynamic characteristics of the chain drive system during the operational process. Firstly, the dynamic simulation model for the mining scraper conveyor’s chain drive system was established in ADAMS, taking into account its structural characteristics. Then, the mathematical model of the permanent magnet synchronous motor was established using the coordinate transformation theory, and the speed controller based on vector control was designed by using the theory related to sliding mode control. The coupling relationship between the chain drive system of the scraper conveyor and the permanent magnet synchronous motor drive system was investigated. Finally, a joint simulation model of the mechanical system and motor control system was created using ADAMS (View 2019) and MATLAB/Simulink (2020a). The dynamic characteristics of the chain drive system were analyzed, and the three typical working conditions of no load, half load, and rated load were considered. The results show that the contact force between the flat ring and the sprocket undergoes an initial increase, followed by a decrease, and finally another increase. As the load increases from no load to full load, there is a marked increase in the contact force between the loaded side chainrings. Due to the polygon effect, both the speed curve of the permanent magnet drive motor and the contact force curve between the ring chains exhibit periodic fluctuations. The research in this paper provides an idea for the coupling analysis of the scraper conveyor electromechanical system.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.