Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. Lightweight diagnosis networks are the core of this technology. However, existing lightweight diagnosis networks have the following limitations. 1) They lack a lightweight learning mechanism for edge computing scenarios to deal with time-scale changes of bearing fault features when trains operate under variational conditions. 2) An adaptive neural network pruning technique synchronized with training is expected to avoid the repetitive and cumbersome processes of traditional pruning techniques. To overcome the above limitations, this paper proposes a new multi-scale lightweight network with adaptive pruning for the intelligent diagnosis fault of train bogie bearings in edge computing scenarios. First, weight-sharing multi-scale convolutions (WSMSCs) are developed to capture multi-time scale features from vibration signals with extremely low computing costs. Then, inverse separable convolution (ISC) blocks are built to further capture high-level features, and an adaptive pruning technique that does not require artificial jobs is proposed to eliminate useless network structures concurrently with training. Finally, diagnostic results are obtained through a dense layer activated by Softmax. Experimental results demonstrate that the accuracy and complexity of the proposed network are superior to other state-of-the-art lightweight bearing fault diagnosis networks under varying operating conditions. Furthermore, the proposed networks can be deployed on more general edge devices.

Continual learning is promising in intelligent fault diagnosis of three-phase motors, which allows diagnosis networks to increase diagnosable fault classes without tedious retraining during various mechanical and electrical fault sample accumulations. Existing studies, however, have the following limitations. 1) The representation learning ability of diagnosis networks is nonaugmentable because of predefined and fixed network structures. 2) The impacts of different working conditions are not explicitly considered during continual learning. To overcome the abovementioned limitations, an elastic expandable continual learning framework is proposed for class-added motor fault diagnosis. First, an elastic expansion mechanism is developed to improve the representation learning ability during continual learning by progressively introducing new network branches. In addition, the new branches are pruned concurrently with training to elastically adjust the network sizes, thereby avoiding bringing excessive computational complexity. Then, an adaptive multiscale convolution mechanism is designed to learn robust fault features by fusing working condition information, increasing the diagnostic accuracy of continual learning under complex working conditions. Finally, a new training strategy with a multiobjective loss is formulated to guide continual learning. The effectiveness of the proposed method is demonstrated through motor fault simulation experiments.

The significance of intelligent fault diagnosis techniques is increasing in maintaining the security and reliability of railway operations. In particular, few-shot learning shows promise since it can address the issue of limited fault samples. However, the existing approaches have the following shortcomings. First, they ignore rich fault information in the joint distributions of multi-dimensional features, limiting the improvement of diagnosis accuracy. Second, they lack specialized mechanisms to alleviate severe degradation in diagnosis accuracy caused by label mistakes in engineering scenarios. To address the above issues, a Brownian distance covariance-based few-shot learning framework of fault diagnosis considering noisy labels is proposed for train transmission systems. In network construction, a novel joint distribution expression (JDE) layer is developed and embedded into the prototypical network, implementing similarity measures based on the joint distribution characteristics. In network training, a new antimislabeling learning strategy is designed for few-shot fault diagnosis tasks, in which a similarity-weighted prototype aggregation mechanism mitigates the negative effects of mislabeled support samples and a loss function with outlier attenuation avoids disruptions from mislabeled query samples. Taking a fault diagnosis study case of motors, gearboxes, and axle boxes as examples, it is demonstrated that the proposed framework can effectively learn and recognize faults, even using limited training datasets containing parts of mislabeled samples. Moreover, the superiority of the proposed framework is demonstrated by comparing it with other state-of-the-art methods.

The reliability and safety of trains have always been the top priority in the railway industry. As the critical subsystem of trains, the health states of bogie transmission systems directly affect the operation safety of trains. Train fault diagnosis, predictive maintenance, and algorithms evaluation become crucial to ensure operational safety. Thus, there is an urgent need for high-quality fault datasets of train transmission systems. This tutorial introduces the first publicly available fault datasets of train transmission systems, referred to as Beijing Jiaotong University - Rail Autonomous Operations (BJTU-RAO) bogie datasets. The datasets contain multi-sensor data streams of transmission systems of subway train bogies including a health state and 50 faulty states, which are acquired by fault simulation experiments and are released at the 2024 Global Reliability and Prognostics and Health Management (PHM) Conference. For each type of state, samples under nine different working conditions are collected to represent the different operating conditions of trains, and the signals are recorded at a sampling frequency of 64 kHz. The datasets are collected, organized, and made publicly available by the research team from the State Key Lab of Advanced Rail Autonomous Operation at BJTU, aiming to encourage scholars and engineers to investigate and validate their fault detection or diagnosis algorithms for key components of train transmission systems, such as driving gearboxes, axle boxes, and traction motors.