Abstract

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