The paper deals with an innovative Predictive Maintenance (PdM) system to assess the quality of the engine oil for buses, tested in Ravenna within the European Bus System of the Future - EBSF_2 project, funded by the European Union. The system relies on a PdM software linked to oil sensors and filters, installed on a test fleet, and on an IT architecture, specifically designed. The system enables a continuous assessment of the oil quality, which is highly predictive of the engine performance, thus detecting potential breakdowns and planning the replacement of spare parts ahead of regular schedules; the system also detects which substances and problems cause the poor quality of the oil. The paper describes the system, the testing scenarios, the performance assessment, and the main outcomes. Results also enable an assessment of additional, potential environmental benefits (especially mitigation of emissions toxicity and improvement of waste management). Additional features are also reported such as an algorithm to estimate the date when oil has to be changed. Such results are analysed and commented with the research objective to provide advanced knowledge for further research studies.
Predictive maintenance for buses. Outcomes and potential from an Italian case study / Corazza, Maria Vittoria; Vasari, Daniela; Petracci, Enrico; Brambilla, Luigi. - 879:(2019), pp. 461-468. (Intervento presentato al convegno Skiathos, Greece tenutosi a 4th Conference on Sustainable Urban Mobility (CSUM2018)) [10.1007/978-3-030-02305-8_56].
Predictive maintenance for buses. Outcomes and potential from an Italian case study
Corazza, Maria Vittoria
;
2019
Abstract
The paper deals with an innovative Predictive Maintenance (PdM) system to assess the quality of the engine oil for buses, tested in Ravenna within the European Bus System of the Future - EBSF_2 project, funded by the European Union. The system relies on a PdM software linked to oil sensors and filters, installed on a test fleet, and on an IT architecture, specifically designed. The system enables a continuous assessment of the oil quality, which is highly predictive of the engine performance, thus detecting potential breakdowns and planning the replacement of spare parts ahead of regular schedules; the system also detects which substances and problems cause the poor quality of the oil. The paper describes the system, the testing scenarios, the performance assessment, and the main outcomes. Results also enable an assessment of additional, potential environmental benefits (especially mitigation of emissions toxicity and improvement of waste management). Additional features are also reported such as an algorithm to estimate the date when oil has to be changed. Such results are analysed and commented with the research objective to provide advanced knowledge for further research studies.File | Dimensione | Formato | |
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