This article presents two approaches to power circuit breakers condition assessment. The first one covers a wide variety of machine learning classification algorithms where the input for the classification is a manually selected feature set. The second one utilizes deep learning classification based on the convolutional neural network. Both approaches revolve around the idea behind spectral kurtosis, one of which exploits its visual representation in the form of kurtogram. The first approach uses a spectral kurtosis curve as the base for feature extraction while the second approach uses a spectral kurtosis kurtogram as a single input into the convolutional neural network. The validation is performed on a large set of vibration signatures and compared to competing state-of-the-art algorithms. The results indicate promising features of the proposed approach.

This paper deals with safe human-robot collaboration in the context of speed and separation monitoring paradigm. The core of the approach is to continuously track the separation distance between the robot and the human. The robot speed is then adjusted according to the perceived distance so that it will be able to stop before eventually come into contact with the human. We present an approach that aims at maximizing the productivity of the robot, i.e., its speed, while keeping the prescribed safety requirements satisfied. The method is based on explicit representation of danger zones – regions around the robot, where safety requirements are violated. The motion is then generated such that the robot moves as fast as possible, while its danger zone still does not collide with human operators. The approach is validated within an experimental study. Note to Practitioners—This article was motivated by the problem of maximizing productivity of the robotic manipulator while ensuring the safety of human collaborator. The increase in productivity is achieved by a faster traversal of predefined paths without compromising the safety of the human, which is specifically defined by industrial standard. The approach requires limited knowledge on robot’s dynamical properties. More precisely, we only need the braking time as a “lumped” representation of robot’s inertia. The underlying optimization problem is conveniently resolved by introducing danger zones that allow for intuitive visualization and geometrical representation of the regions around the robot that must be avoided. On the other hand, the method assumes the representation of humans via typical geometric primitives, which can be obtained using of-the-shelf depth perception systems. The solution to the problem reduces to a repeated collision checking between danger zones and the human. Such an approach turns out to be suitable for real-time implementation due to availability of fast and efficient collision checking algorithms/libraries.

Abstract The importance of the high voltage circuit breaker for the power system’s safe and reliable operation is paramount. This research aims to analyse and provide the most significant high voltage circuit breaker health state indices based on the non-invasive vibration fingerprint measurement method. Results obtained and presented in this paper are validated on the data set acquired from the vacuum circuit breaker.