Abstract
This paper is focused on the design and development of a smart and compact electronic control unit (ECU) for the monitoring of a bus fleet. The ECU system is able to extract all vehicle data by the on-board diagnostics-(ODB)-II and SAE J1939 standards. The integrated system Internet of Things (IoT) system, is interconnected in the cloud by an artificial intelligence engine implementing multilayer perceptron artificial neural network (MLP-ANN) and is able to predict maintenance of each vehicle by classifying the driver behavior. The key performance indicator (KPI) of the driver behavior has been estimated by data mining k-means algorithm. The MLP-ANN model has been tested by means of a dataset found in literature by allowing the correct choice of the calculus parameters. A low means square error (MSE) of the order of 10−3 is checked thus proving the correct use of MLP-ANN. Based on the analysis of the results, are defined methodologies of key performance indicators (KPIs), correlating driver behavior with the engine stress defining the bus maintenance plan criteria. All the results are joined into a cloud platform showing fleet efficiency dashboards. The proposed topic has been developed within the framework of an industry research project collaborating with a company managing bus fleet.
Highlights
The controller area network (CAN) bus and on-board diagnostics (ODB) communication interfacesII are standards typically used to extract information from a vehicle [1,2,3], to control the vehicle conditions, and to deduce any anomalies by accessing the electronic control unit (ECU)
Data of vehicles are transmitted in the cloud to a data mining engine performing driver key performance indicator (KPI) by defining a score using k-means clustering analysis, and by predicting engine stress through multilayer perceptron artificial neural network (MLP-artificial neural networks (ANN)) algorithms
The output of the data mining algorithms allowed the establishment of criteria for the predictive maintenance, anticipating the maintenance in cases of predicted engine stress due to incorrect driver behavior
Summary
The controller area network (CAN) bus and on-board diagnostics (ODB) communication interfacesII are standards typically used to extract information from a vehicle [1,2,3], to control the vehicle conditions, and to deduce any anomalies by accessing the electronic control unit (ECU). In [8], the integrability of the sensors in a complete diagnostic network has been demonstrated, suggesting ideating a smart and compact unit adaptable to different types of vehicles by using specific connectors. In this scenario, ODB II connector has been adopted in [9,10] to acquire data for the fuel consumption trend, which could indirectly provide indications on the analysis of drivers behavior and in a certain way is able to predict the vehicle wear (the fuel consumption is a parameter that is a function of the acceleration and of the vehicle speed). The analysis, mainly derived from the speed and consumption indications of a bus, can be carried out by analyzing the data acquired by global positioning systems (GPS) [11,12,13,14,15,16,17,18,19], IoT 2020, 1, 180–197; doi:10.3390/iot1020012 www.mdpi.com/journal/iot
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