There are major discussions about the vulnerability of protocols based on Real Time Ethernet (RTE) and techniques for detecting anomalies. Thus, this work proposes a methodology for detecting anomalies by optimizing the data extraction and by classifying traffic-related features. In order to cope with this proposal, an ANN-based classifier is trained using selected relevant features. These features are extracted using variable sized sliding window and selected according to their correlation with the other features and the expected output of the ANN. The number of relevant features can vary according to performance indicators of the classifier. The proposed methodology was exploited for identifying four different events of PROFINET networks and the performance of the classifier was considered successful for all cases. This outcome suggests that the proposed methodology may be successful for anomalies detection in any PROFINET network. However, the application of the proposed methodology to other RTE protocol is foreseen.

SUMMARY: Novel kinematic architectures can be alternatives for designing energy efficient robotic systems. In this work, the impact of kinematic redundancies in the energy consumption of a planar PKM, the 3PRRR manipulator, is experimentally verified. Because of the presence of the kinematic redundancies, the inverse kinematic problem presents infinity solutions. In this way, a redundancy resolution scheme based on the Model Predictive Control technique is proposed and exploited. It can be concluded that the energy consumption of the non-redundant parallel manipulator 3RRR for executing predefined tasks can be considerably reduced by the inclusion of kinematic redundancies.