Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains. The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears. In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model. Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness. Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability. Finally, the validity of the algorithm is verified by the actual running gears system.

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

Aiming at the problem of colored noise in the signal, this article proposes a subspace frequency estimation approach under colored noise with application to fault diagnosis of motor rolling bearings. First, a nonlinear discrete-time system is described to generate colored noise. An extended I/O model with parameters of a nonlinear discrete-time system is given by the subspace method. Then, the gap metric-aided system order determination approach is developed for extended observability matrix identification. Then, the data-driven diagnostic observer parameter identification approach and the fast approximate power iterative subspace method are adopted to realize online monitoring for frequency change detection. Eventually, a data-driven design scheme of residual generator is proposed for the implementation of fault detection. The effectiveness of the proposed methods is verified for fault diagnosis performance through numerical simulations and the experimental measurements from the dynamic motor rolling bearing experiment rig.

The electrical drive system of high-speed trains is a key subsystem to ensure the continuous supply of train power and stable operation. By the use of local information, this article presents a method called multiblock dynamic slow feature analysis (MBDSFA) with its application in the electrical drive system of high-speed trains. First, the relevance among all variables of electrical drive systems is calculated by using mutual information, based on which all variables are divided into blocks. Then, the dynamic slow feature analysis-based system monitoring scheme is employed for each subblock, and the local characteristics of electrical drive systems are analyzed via two kinds of test statistics. All subblocks are integrated based on the Bayesian inference to obtain the global monitoring results. Finally, the effectiveness and feasibility of the proposed approach are verified through the case study on the electrical drive system of high-speed trains.

Electrical drive systems are the core of high-speed trains, providing energy transmission from electric power to traction force. Therefore, their safety and reliability topics are always active in practice. Among the current research, fault injection (FI) and fault diagnosis (FD) are representative techniques, where FI is an important way to recur faults, and FD ensures the recurring faults can be successfully detected as soon as possible. In this paper, a tutorial on a hardware-implemented (HIL) platform that blends FI and FD techniques is given for electrical drive systems of high-speed trains. The main contributions of this work are fourfold: (1) An HIL platform is elaborated for realistic simulation of faults, which provides the test and verification environment for FD tasks. (2) Basics of both the static and dynamic FD methods are reviewed, whose purpose is to guide the engineers and researchers. (3) Multiple performance indexes are defined for comprehensively evaluating the FD approaches from the application viewpoints. (4) It is an integrated platform making the FI and FD work together. Finally, a summary of FD research based on the HIL platform is made.