The hunting phenomenon is an intrinsic swaying motion appearing in railway vehicles due to the vehicle’s forward speed and the wheel–rail contact forces. Hunting motion consists of wheelset and other vehicle’s components oscillations that arise above a certain vehicle’s speed known as critical or hunting speed. These oscillations are of unstable nature and represent a safety hazard as they could lead to the vehicle’s derailment. This article analyses the stability of a bogie nonlinear model for a Spanish high-speed train when this is travelling at speeds near the hunting speed. The vehicle’s stability is studied by means of root loci methods, and the value of the critical speed is found. In addition to this, the behaviour of the vehicle is studied in both stable and unstable regions and the existence of limit cycles is discussed in this work. Finally, a sensitivity analysis of the axle load and suspension parameters is performed. The results show that the axle load, the vertical stiffness of the primary suspension and the lateral damping of the secondary suspension have a significant influence on the value of the critical speed.

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.

The fourth industrial revolution is changing the way industries face their problems, including maintenance. The railway industry is moving to adopt this new industry model. The new trains are designed, manufactured, and maintained following an Industry 4.0 methodology, but most of the current trains in operation were not designed with this technological philosophy, so they must be adapted to it. In this paper, a new methodology for adapting a high-speed train to Industry 4.0 is proposed. That way, a train manufactured before this new paradigm can seize the advantages of Maintenance 4.0. This methodology is based on four stages (physical system, digital twin, information and communication technology infrastructure, and diagnosis) that comprise the required processes to digitalize a railway vehicle and that share information between them. The characteristics that the data acquisition and communication systems must fulfil are described, as well as the original signal processing techniques developed for analysing vibration signals. These techniques allow processing experimental data both in real time and deferred, according to actual maintenance requirements. The methodology is applied to determine the operating condition of a high-speed bogie by combining the signal processing of actual vibration measurements taken during the normal train operation and the data obtained from simulations of the digital twin. The combination of both (experimental data and simulations) allows establishing characteristic indicators that correspond to the normal running of the train and indicators that would correspond to anomalies in the behaviour of the train.

High-speed rail is a key player in people's mobility thanks to its speed and high safety standards. These high safety standards are supported by extensive maintenance (of both rolling stock and track) that applies the latest nondestructive and monitoring techniques. In that context, this work presents the enhanced graphical representation of state configurations (EGRSC) and enhanced chromogram of bands of frequency (ECBF) methods, which are the evolution of the graphical representation of State configurations and chromogram of bands of frequency techniques. A clearer nomenclature is defined for the configuration states of the EGRSC. The ECBF introduces a new variable threshold that allows discarding frequency regions with small power variations and a new color palette. These methods are used in the vibratory study of an in-service high-speed train to identify the operating condition of the train. To conduct that task, a set of accelerometers is installed on the axle box of a trailer axle of the train. Accelerometers are oriented into the three space directions: longitudinal, lateral, and vertical. Vibration signals are taken during the normal operation of the train and transmitted to and stored on a remote database. Vibration measures are collected before and after a major maintenance operation. The application of EGRSC and ECBF allows collecting information from thousands of vibration signals and to display that information intuitively and understandably. The analysis of EGRSC and ECBF results allows identifying several frequency regions with significant spectral power variations that are common to the three vibration directions studied. These patterns should be taken as a reference for future maintenance actions. Any unexpected behavior of these frequency regions would indicate the presence of a fault.