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Induction motors (IM) as a critical component of many industrial processes are subjected to issues such as aging motors, high reliability requirements, and cost competitiveness. Therefore, many research efforts have been focused on fault detection in IMs. The main specification of this paper involves fault detection of three phase IMs using vibration and electrical current setup. This paper compares the results obtained by vibration and electrical current setup in order to a better understanding of fault detection setups’ operation in induction machines. The experimentation was performed on two faulty and one healthy squirrel cage motor. A number of data was captured through the labVIEW software. Principal Component Analysis (PCA) was employed for feature extraction to classify the faults of IMs. Most vibration hardware systems are relatively costly and difficult to set up, but they resulted in significantly higher accurate and classified data in comparison to the results of current setup.

Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers' behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling.

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