Latest revision as of 21:39, 3 February 2021

Abstract

The paper focuses on the methodology and the test process adopted in Ravenna and Forlì (Italy), within the
EBSF_2 research project, where a demonstrator to improve predictive management performance was tested. This
relied on a maintenance software to analyze data coming from sensors assessing the engine oil quality, therefore
detecting potential breakdowns and replacing spare parts in advance; the system also identifies which metals or
problems concurred to the oil poor quality. The test involves three urban diesel-fueled urban buses in Forlì and
three methane-fueled urban buses in the Ravenna, for a total of 27 lines and a maintenance staff of 20 units, over
an 12-month testing period. Since the initial results, the sensor-based system proved to be effective. Test results
are analyzed and operational and environmental benefits highlighted, with the research goal to advance scientific
knowledge in this field.

Original document

The different versions of the original document can be found in:

• https://zenodo.org/record/1421640 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://dx.doi.org/10.5281/zenodo.1421639,

https://zenodo.org/record/1421640 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://dx.doi.org/10.5281/zenodo.1421640,

https://zenodo.org/record/1421640 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

DOIS: 10.5281/zenodo.1421639 10.5281/zenodo.1421640