Recently, deep learning based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks lack of interpretability for fault diagnosis tasks. That is, how to understand and learn discriminant fault features from complex monitoring signals remains a great challenge. Considering this challenge, this paper explores the use of attention mechanism in fault diagnosis networks and designs attention module by fully considering characteristics of rolling bearing faults to enhance fault-related features and to ignore irrelevant features. Powered by the proposed attention mechanism, a multi-attention one-dimensional convolutional neural network (MA1DCNN) is further proposed to diagnose wheelset bearing faults. The MA1DCNN can adaptively recalibrate features of each layer and can enhance the feature learning of fault impulses. Experimental results on the wheelset bearing dataset show that the proposed multi-attention mechanism can significantly improve discriminant feature representation, thus the MA1DCNN outperforms eight state-of-the-arts networks.

Rail surface defects (RSDs) are a major problem that reduces operation safety. Unfortunately, the existing RSD detection systems have very limited accuracy. Current image processing methods are not tailored for the railway track and many fully convolutional networks (FCN)-based methods suffer from the blurry rail edges (RE). This paper proposes a new rail boundary guidance network (RBGNet) for salient RS detection. First, a novel architecture is proposed to fully utilize the complementarity between the RS and the RE to accurately identify the RS with well-defined boundaries. The newly developed RBGNet injects high-level RS object information into shallow RS edge features by a progressive fused way for obtaining fine edge features. Then, the system integrates the refined edge features with RS features at different high-level layers to predict the RS precisely. Second, an innovative hybrid loss consisting of binary cross entropy (BCE), structural similarity index measure (SSIM), and intersection-over-union (IoU) is proposed and equipped into the RBGNet to supervise the network and learn the transformation between the input and ground truth. The input and ground truth then further refine the RS location and edges. Conveniently, an image-based model for RSD detection and quantification is also developed and integrated for an automatic inspection purpose. Finally, experiments conducted on the complex unmanned aerial vehicle (UAV) rail dataset indicate the system can achieve a high detection rate with good adaptation capability in complicated environments.

Accurate and real-time fault diagnosis (FD) and working conditions identification (WCI) are the key to ensuring the safe operation of mechanical systems. We observe that there is a close correlation between the fault condition and the working condition in the vibration signal. Most of the intelligent FD methods only learn some features from the vibration signals and then use them to identify fault categories. They ignore the impact of working conditions on the bearing system, and such a single-task learning method cannot learn the complementary information contained in multiple related tasks. Therefore, this article is devoted to mining richer and complementary globally shared features from vibration signals to complete the FD and WCI of rolling bearings at the same time. To this end, we propose a novel multitask attention convolutional neural network (MTA-CNN) that can automatically give feature-level attention to specific tasks. The MTA-CNN consists of a global feature shared network (GFS-network) for learning globally shared features and $K$ task-specific networks with feature-level attention module (FLA-module). This architecture allows the FLA-module to automatically learn the features of specific tasks from globally shared features, thereby sharing information among different tasks. We evaluated our method on the wheelset bearing data set and motor bearing data set. The results show that our method has a better performance than the state-of-the-art deep learning methods and strongly prove that our multitask learning mechanism can improve the results of each task.

In recent years, deep learning has been proven to be a promising bearing fault diagnosis technology. However, most of existing methods are based on single-task learning. Fault diagnosis task is treated as an independent task, and rich correlation information contained in different tasks is ignored. Therefore, this paper explores possibility of using speed identification and load identification tasks as two auxiliary tasks to improve performance of the fault diagnosis task, and proposes a multi-task one-dimensional convolutional neural network (MT-1DCNN). Specifically, the MT-1DCNN utilizes trunk network to learn shared features required for every task, and then processes different tasks through multiple taskspecific branches. In this way, the MT-1DCNN can utilize features learned by related tasks to improve performance of the fault diagnosis task. Experiment results with wheelset bearing dataset show that the multi-task learning can make full use of the feature information captured by the speed identification and the load identification tasks to improve fault diagnosis performance of the network, and the MT-1DCNN has better performance than five excellent networks in accuracy.

Railway intrusion seriously threatens railway safety and can cause enormous loss of life and property. Therefore, railway intrusion detection is crucial for the safety of railway operation. Among the current methods of intrusion detection, machine vision-based methods have been widely used in railways, and have attracted close attention because of their great benefits. This paper proposes a comprehensive review of railway intrusion detection based on machine vision, covering ground monitoring, on-board inspection, and unmanned aerial vehicle (UAV) inspection. First, this paper systematically reviews most of the studies over the past two decades and presents the survey in three parts. Second, by analyzing these studies and the requirements for railway monitoring, we summarize the major challenges that hinder railway intrusion detection based on machine vision. Finally, we propose several promising perspectives for railway intrusion detection based on machine vision by comprehensively considering the development of machine vision, sensors, and pattern recognition together with the needs of railway scenes.

Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation threshold for defects detection. Two visual comparison experiments were carried out to demonstrate the efficiency of the proposed methods. Finally, a quantitative comparison experiment shows the LWLC-GSME model achieves a recall of 93.75% for T-I defects and of 94.26% for T-II defects. Therefore, LWLC for image enhancement, in conjunction with GSME for defects segmentation, is efficient and feasible for the detection of rail surface defects based on UAV Images.