Rail surface defect detection plays a critical role in the maintenance of the rail transportation system. Video analysis technology is a promising method to detect defects due to its low cost and effectiveness. Several attempts with hand‐craft features have been made to obtain the detection results by using traditional machine vision algorithms. However, these methods suffer from imprecise results due to challenging conditions, such as deteriorated and changeable lighting environment and various types of complex rail surface defects. Recently, classification methods with complex deep convolutional networks have become popular. Despite their high accuracy, these methods cannot meet the requirements of defects localisation and real‐time processing in practice. To solve these problems, this study proposes a novel object detection algorithm to detect rail defects. The net architecture of the proposed algorithm includes a backbone network using MobileNet and several novel detection layers with multi‐scale feature maps inspired by you only look once (YOLO) and feature pyramid networks. Two different architectures of MobileNet are used to estimate the performance of defects detection. The experimental results demonstrate the great potential of the proposed algorithm with fast inference speed and high accuracy in the industry.

High-speed blades are often prone to fatigue due to severe blade vibrations. In particular, synchronous vibrations can cause irreversible damages to the blade. Blade tip-timing methods (BTT) have become a promising way to monitor blade vibrations. However, synchronous vibrations are unsuitably monitored by uniform BTT sampling. Therefore, non-equally mounted probes have been used, which will result in the non-uniformity of the sampling signal. Since under-sampling is an intrinsic drawback of BTT methods, how to analyze non-uniformly under-sampled BTT signals is a big challenge. In this paper, a novel reconstruction method for non-uniformly under-sampled BTT data is presented. The method is based on the periodically non-uniform sampling theorem. Firstly, a mathematical model of a non-uniform BTT sampling process is built. It can be treated as the sum of certain uniform sample streams. For each stream, an interpolating function is required to prevent aliasing in the reconstructed signal. Secondly, simultaneous equations of all interpolating functions in each sub-band are built and corresponding solutions are ultimately derived to remove unwanted replicas of the original signal caused by the sampling, which may overlay the original signal. In the end, numerical simulations and experiments are carried out to validate the feasibility of the proposed method. The results demonstrate the accuracy of the reconstructed signal depends on the sampling frequency, the blade vibration frequency, the blade vibration bandwidth, the probe static offset and the number of samples. In practice, both types of blade vibration signals can be particularly reconstructed by non-uniform BTT data acquired from only two probes.

The rapid development of IoT technology has promoted the integration and networked fusion of massive heterogeneous sensors. However, traditional Gaussian-Markov frameworks struggle to characterize nonlinear long-term correlated errors within systems and network cooperative effects, limiting inter-node interoperability. To address this, we propose a generalized nonlinear stochastic process (GNSP) theoretical framework, constructing nonlinear operators with long-term memory characteristics, and employing fractional calculus tools to achieve unified analysis of multiple error types within and across nodes. Based on this, we derive distributed error propagation laws on Lie group spaces, establish boundary criteria based on Wasserstein metrics, and improve nonlinear filters to design a dual-layer fusion architecture combining single-node hybrid adaptive filtering and inter-node collaborative optimization. Using vehicle networking GNSS/INS systems as an example, experimental results show that the proposed single-node strategy improves position, velocity, and attitude accuracy by an average of 11.71%, 9.82%, and 12.83% respectively in complex environments such as urban canyons and mountainous forest areas. Meanwhile, through inter-node parameter sharing and cooperative strategies of federated filtering, further V2V collaboration improves performance by 8.5%-12.7%, maintaining average improvements of 16.85%, 18.37%, and 21.14% even under limited communication conditions. This effectively addresses the challenges of distributed sensing errors in IoT environments.

During the operation of the railway system, it is inevitable that there will be track defects that affect train operation safety, especially the defects of the rail and fastener. Therefore, it is necessary to detect these defects in time to ensure the safety of train operations. In recent years, with the development of deep learning and computer vision technology, intelligent detection of track defects has made great progress. However, the existing methods not only suffer from the scarcity of defective samples but also their fine-grained recognition is low due to subtle differences between similar classes; furthermore, their real-time performance also needs to be improved. To solve these problems, this article presents a real-time defect detection method for track components based on instance segmentation. First, an improved lightweight instance segmentation network is proposed for the segmentation and location of fastener and rail. Second, a method that detects fastener defects by analyzing the geometric features of the fastener masks is proposed, which can overcome the scarcity of defective fastener samples. A cascade defect detection network is proposed to realize multiresolution and high-resolution detection of rail, which improves the accuracy of rail defect detection. Finally, the TensorRT inference framework is used to accelerate the defect detection network and realize edge end deployment. The experimental results show that the proposed method can achieve 95.1% recall rate of fastener defects, 98% detection rate of rail defects, and 93.5% classification accuracy of rail defects; 33.4 FPS defect detection speed is implemented in the Jetson AGX Xavier embedded device, which verifies the accuracy and real-time performance of the proposed method.

Railway surface defect detection is an important technical measure to ensure the safe operation of the rail transit system. Due to the complex and diverse features of rail surface defects and the uneven curvature of the image caused by the track surface, it is difficult to obtain better detection results by traditional machine vision technology. The existing deep learning-based method only classifies the defect image and cannot locate the defect location. At the same time, the network structure of this method is complex, and the detection accuracy and speed cannot be well balanced. This paper proposes a new method for detecting railway surface defects based on end-to-end target detection and lightweight convolutional network structure. This method uses MobileNetV2 as the backbone network to extract image features, and then performs multi-scale defect detection to ensure real-time. Under the premise of detection, higher detection accuracy can be obtained.