Due to the rapid development of high-speed railways, there is an extensive need for the condition monitoring of the stability of vehicles. Considering the weakness of the current alarm standards, a new index is introduced for describing the periodicity of the state variable series in the nonlinear dynamics system, and a new guideline – based on the proportion distribution of the index – is proposed for detecting the hunting and for evaluating the stability performance in both the space and time domains. Then, the selection of algorithm parameters is conducted and the generalization ability is validated; a number of simulations and tests are applied to further verify its effectiveness. The advantages of this new method, such as an accurate alarm for hunting motions, evaluation of vehicle dynamics, robustness and feasibility of the real time application, are highlighted. This method can further contribute to the prognostic and health management of railway vehicles through early warning and condition assessment.

Railway vehicle dampers undergo degradation and failure after long-term service. Bench tests in this paper indicate a stochastic failure process, in which damping characteristics follow bimodal random distributions between normal and failure; failure probability increases in the ageing process until it becomes deterministic. This failure mode may be neglected due to the probabilistic absence of failure, which poses great risk to reliability. Then, nonlinear suspension dynamics with stochastic failure is modelled; Itô stochastic differential rule and cumulant-neglect closure are adopted to derive moment equations. The scheme of progressive iteration is proposed to obtain fast-convergent and stable solutions for analysing the effect of stochastic failure process on vibration response. Further, numerical simulation of a vehicle with stochastic failure is performed for failure effects analysis on dynamical performance. The results demonstrate that the deterioration of failure probability and damping reduction amplitude leads to larger vibrations. The stochastic failure of secondary lateral damper and anti-yaw damper is harmful for lateral vehicle dynamics, while that of secondary vertical damper has negative effects on vertical ride quality. It is of great importance to pay full attention to the stochastic failure process of dampers for reliability and security of railway vehicles.

Plenty of studies focused on single-direction active suspensions on railway vehicles; at least four actuators per vehicle are needed forconventional active secondary suspensions in both lateral and vertical directions, leading to a sharp increase in costs and reduction inreliability. This paper proposes a novel configuration, lateral-vertical coupled (LVC) active secondary suspension, with only two actuators and two accelerometers per vehicle. To deal with the coupled dynamics, linear quadratic Gaussian (LQG) method and optimal skyhook damping (OSD) method are developed for achieving optimal control. OSD method has similar advantages as LQG method and is easier to implement. Then, linearised vehicle dynamics is modelled to design the controllers and observer, and a more complete nonlinear simulation model is established for verification. Theoretical analysis and simulation results show that LVC active suspension reduces car body vibration in one direction at the cost of negative effects in the other. LQG control fails to provide satisfying performance compared with passive suspensions, whereas OSD control can effectively improve lateral and vertical vibration and ride comfort by applying reasonable actuator forces. Besides, actuator dynamics is important to the effectiveness of LVC active suspension, and OSD controller is robust to measurement noise and model errors to some extent.

Operational modal analysis (OMA) enables the identification of modal characteristics under operational loads and conditions. Traditional frequency-domain methods cannot directly capture modal changes over time, while existing time-frequency representations are not sufficiently interpretable due to spurious modes and implicit parameter design. This paper develops a new OMA method in time-frequency representation based on frequency-domain decomposition (FDD). Short-time FDD and a convolution-based strategy are proposed to obtain singular values and local mode shape similarity, respectively, which are further fused into mode indicators by a fuzzy-based strategy mimicking the modal assurance criterion. The method provides not only a global view of the modal characteristics over time and frequency but also estimates of the modal parameters. It is applicable to strongly nonstationary responses under time-varying loads and conditions. All the parameters explicitly affect the time-frequency representation, and the interpretability is enhanced by including physical information from the user’s prior knowledge in selecting parameters and peak bands. The proposed method is validated based on a study of railway sleepers under train passage. The rigid-body motions and bending modes are identified at frequencies up to 6,500 Hz in laboratory tests and 4,500 Hz in field tests at speeds up to 200 km/h. The identified natural frequencies and mode shapes agree with experimental modal analysis (EMA). The proposed method outperforms EMA in terms of broad frequency range and low measurement cost and can be potentially applied to structural health monitoring under operational conditions.