Browsing by Subject "Heavy-Duty Vehicles"

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    Multi-Class Vocation Identification for Heavy Duty Vehicles
    (2021-12) Yadav, Varun; Ben-Miled, Zina; Dos Santos, Euzeli; Salama, Paul
    Understanding the operating profile of different heavy-duty vehicles is needed by parts manufacturers for improved configuration and better future design of the parts. This study investigates the use of a tournament classification approach for both vocation and fleet identi- fication. The proposed approach is implemented using four different classification techniques, namely, K-Means, Expectation Maximization, Particle Swarm Optimization, and Support Vector Machines. Vocations classifiers are developed and tested for six different vocations ranging from coach buses to rail inspection vehicles. Operational field data are obtained from a number of vehicles for each vocation and aggregated over a pre-set distance that varies according to the data collection rate. In addition, fleet classifiers are implemented for five fleets from the coach bus vocation using a similar approach. The results indicate that both vocation and fleet identification are possible with a high level of accuracy. The macro average precision and recall of the SVM vocation classifier are approximately 85%. This result was achieved despite the fact that each vocation consisted of multiple fleets. The macro average precision and recall of the coach bus fleet classifier are approximately 77% even though some fleets had similar operating profiles. These results suggest that the proposed classifier can help support vocation and fleet identification in practice.
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    Vocation Clustering for Heavy-Duty Vehicles
    (2020-12) Kobold, Daniel, Jr.; Ben-Miled, Zina; King, Brian S.; Dos Santos, Euzeli C.
    The identification of the vocation of an unknown heavy-duty vehicle is valuable to parts manufacturers who may not have otherwise access to this information on a consistent basis. This study proposes a methodology for vocation identification that is based on clustering techniques. Two clustering algorithms are considered: K-Means and Expectation Maximization. These algorithms are used to first construct the operating profile of each vocation from a set of vehicles with known vocations. The vocation of an unknown vehicle is then determined using different assignment methods. These methods fall under two main categories: one-versus-all and one-versus-one. The one-versus-all approach compares an unknown vehicle to all potential vocations. The one-versus-one approach compares the unknown vehicle to two vocations at a time in a tournament fashion. Two types of tournaments are investigated: round-robin and bracket. The accuracy and efficiency of each of the methods is evaluated using the NREL FleetDNA dataset. The study revealed that some of the vocations may have unique operating profiles and are therefore easily distinguishable from others. Other vocations, however, can have confounding profiles. This indicates that different vocations may benefit from profiles with varying number of clusters. Determining the optimal number of clusters for each vocation can not only improve the assignment accuracy, but also enhance the computational efficiency of the application. The optimal number of clusters for each vocation is determined using both static and dynamic techniques. Static approaches refer to methods that are completed prior to training and may require multiple iterations. Dynamic techniques involve clusters being split or removed during training. The results show that the accuracy of dynamic techniques is comparable to that of static approaches while benefiting from a reduced computational time.