Testing an innovative predictive management system for bus fleets: outcomes from the Ravenna case study. Issue 4 (4th January 2018)

• Record Type: Journal Article
• Authors: Corazza, Maria Vittoria; Magnalardo, Silvia; Musso, Antonio; Petracci, Enrico; Tozzi, Michele; Vasari, Daniela; de Verdalle, Emmanuel
• Is Part Of: IET intelligent transport systems. Volume 12:Issue 4(2018)
• Page Start: 286
• Page End: 293
• Publication Date: 2018-01-04
• Subjects: maintenance engineering -- data analysis -- intelligent transportation systems; predictive management system -- bus fleets -- Ravenna -- Italy -- predictive maintenance management -- maintenance software -- data analysis -- CANbus -- sensors -- oil quality assessment -- potential breakdown detection -- spare parts replacement -- fuel consumption -- costs -- intelligent transport system -- European Bus System of the Future -- EBSF_2; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2017.0207
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : The European Bus System of the Future (EBSF_2) is a research project funded by the European Union with the aim of developing a new generation of buses across Europe. The goals are to increase the attractiveness and efficiency of buses by testing advanced operational and technological solutions. Among these are more comfortable internal layouts, the implementation of a standard IT architecture, intelligent garage procedures, energy‐efficient auxiliaries and green driving assistance systems, all of which are being tested in several demonstrators. This study focuses on the innovative features of the methodology and test process adopted in Ravenna, Italy, where a demonstrator is being used to improve predictive maintenance management. The demonstrator involves maintenance software used to analyse data from CANbus and sensors to assess oil quality, detect potential breakdowns and, on this basis, replace spare parts in advance. The system also detects which substances contribute to poor oil quality. The study describes performance before and during the implementation of the demonstrator in relation to various impact areas: maintenance, operations, fuel consumption, costs, staff training and the efficiency of the intelligent transport system in processing data. The achieved results are reported with the aim of providing advanced knowledge for applications beyond EBSF_2.