The hydraulic solenoid valve is an essential electromechanical component used in various industries to control the flow rate, pressure, and direction of hydraulic fluid. However, these valves can fail due to factors like electrical issues, mechanical wear, contamination, seal failure, or improper assembly; these failures can lead to system downtime and safety risks. To address hydraulic solenoid valve failure, and its related impacts, this study aimed to develop a nondestructive diagnostic technology for rapid and accurate diagnosis of valve failures. The proposed approach is based on a data-driven model that uses voltage and current signals measured from normal and faulty valve samples. The algorithm utilizes a convolutional autoencoder and hypersphere-based clustering of the latent variables. This clustering approach helps to identify patterns and categorize the samples into distinct groups, normal and faulty. By clustering the data into groups of hyperspheres, the algorithm identifies the specific fault type, including both known and potentially new fault types. The proposed diagnostic model successfully achieved an accuracy rate of 98% in classifying the measurement data, which were augmented with white noise across seven distinct fault modes. This high accuracy demonstrates the effectiveness of the proposed diagnosis method for accurate and prompt identification of faults present in actual hydraulic solenoid valves.

In order to verify the reliability of drive components for industrial robots, component-level life tests must be accompanied by a system-level life test using actual robots in which predefined robot motions are repeated throughout the test. To properly verify the durability of drive components through a system-level life test, it is important to design test modes so that the required test time is the same for all joint drive components of the robot, and it is necessary to design test modes with a high acceleration factor so as to shorten the required test time as much as possible. To solve this problem, the present research proposes a method for designing robot motions that makes the accelerated life test time for all the drive components of the robot equal. In particular, we solve a dynamic based motion optimization problem for an industrial 6-DoF (degrees-of-freedom) robot that minimizes the AM-GM (arithmetic mean to geometric mean) ratio of the acceleration factors of each joint. The results show that $C^2$-continuous test modes with the same acceleration factor, which is inversely proportional to the cycle time of the robot motion, can be derived.

Recently, various studies related to the development of unmanned vehicles have been conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed a method for the path generation of UGVs in a system in which one operator controls many different types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the ground control station. In addition, the DCS must upload the current location, posture, state, etc., as well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a safe path generation algorithm with a short computation time is required. Among the various path generation methods, the potential field algorithm was selected, and the algorithm was modified to reduce the computation time. The computation time before and after modification of the algorithm was obtained and compared through simulation, and the algorithm was verified through application to an actual system by performing an obstacle avoidance experiment and a simultaneous control experiment for two UGVs.