Methodology for the integration of a high-speed train in Maintenance 4.0. Issue 6 (26th November 2021)

• Record Type: Journal Article
• Authors: Bustos, Alejandro; Rubio, Higinio; Soriano-Heras, Enrique; Castejon, Cristina
• Is Part Of: Journal of computational design and engineering. Volume 8:Issue 6(2021)
• Page Start: 1605
• Page End: 1621
• Publication Date: 2021-11-26
• Subjects: Industry 4.0 -- Maintenance 4.0 -- high-speed train -- railways -- digital twin; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwab064
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: 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.