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.

Virtual sensing is an effective method to identify the inaccessible state of the structural systems by compensating the limitations of the conventional physical sensing techniques. Recently, it becomes popular in structural vibration field and their relevant physical domains such as civil, mechanical, aerospace engineering, thermal dynamics, and acoustics. This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling are used to avoid two times larger degrees of freedom within the state-space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the numerical test, the system could identify the status of the motor housing structure in the speed of 16,181 . The FDE and RMSE values are bounded under 0.1816 and 1503.2. In the case of the experimental test, the algorithm is implemented to the beam structure using a single-board computer, including inverse force identification and unmeasured response prediction. Even in the limited computational environment, the system could identify the applied forces in real time in the speed of 2,173 . Through all experimental cases, FDE and RMSE values are bounded under 0.2925 and 0.1660. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.

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.

This paper presents an ongoing project on the development of an e-Engineering framework proposed by KIMM (Korea Institute of Machinery and Materials). The framework is based on a number of advanced technologies, such as intelligent software agents, Web Services, and Service-Oriented Architecture. And it aims to provide an integrated design environment to support integrating personnel, design activities and engineering resources during product development process. Especially Service-Oriented Architecture and Web services technologies enable to utilize and integrate effectively various engineering resources on the geographically distributed computing environments. And intelligent agent technologies support cooperation and coordination mechanisms for the engineering resources and services. In this paper we describe an implementation of the framework in detail.

Of all fluid film bearing types, tilting pad bearings offer the optimal rotordynamic stability due to their excellent stiffness and damping characteristics. Due to the reduction in cross-coupling stiffness, tilting pad bearings are ideal for use in high-speed and high-load turbomachineryapplications, such as industrial compressors and steam/gas turbines. In this investigation, we propose the method to diagnose the main failure mode of tilting pad journal bearings such as babbitt metal wear and pivot wear in bearing pads. Various experimental cases for bearing wearwere precisely implemented through a high-speed rotating test rig operating at 10,200 rpm. The vibration and temperature data were obtained through a condition monitoring system and various features from shaft vibration, bearing metal and lubrication temperature data were extracted for the fault diagnosis. In this study, we have shown that even defects due to the abrasive wear of fluid film bearings including general rotor faults, such as unbalance, misalignment and rubbing, can be accurately diagnosed.