• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.1
• /
• pp.101-111
• /
• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.

• Journal of the Korean Society for Railway
• /
• v.18 no.3
• /
• pp.241-251
• /
• 2015

Compared with ballasted track (BT), ballasted asphalt track (BAT) has been increasingly adopted in many countries due to its more greatly reduced reinforced roadbed thickness and smaller cumulative plastic deformation, and its advantages in terms of maintenance. In this respect, the authors' previous research includes analysis of BAT sections that show performance similar to that of BT sections of the present specifications; reliability verification of the analysis results through real-sized static and dynamic train-load tests were performed. Based on previous research, this paper estimates the track lifetime using the strain of the lower roadbed according to reinforced roadbed thickness; using probabilistic LCC analysis, this paper presents a BAT section that satisfies the design lifetime and that has performance similar to or higher than that of BT.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.239-240
• /
• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.6
• /
• pp.170-177
• /
• 2020

This study examined the clamping force control method and the braking performance test results of an electromechanical brake (EMB) using braking test equipment. Most of the studies related to EMBs have been carried out in the automotive field, dealing mainly with the static test results for various control methods. On the other hand, this study performed a dynamic performance evaluation. The three-phase interior permanent magnet synchronous motor (IPMSM) was applied to drive the actuator of the EMB, and the analysis was verified by JMAG(Ver. 18.0), which is finite element method (FEM) software. The current control, speed control, and position control were used for clamping force control of the EMB, and the maximum torque per ampere (MTPA) control was applied to the current controller for efficient control. The EMB's emergency braking deceleration performance was tested in the same way as conventional pneumatic brake systems when the wheel of a train rotates at 110 km/h, 230 km/h, and 300 km/h. The emergency braking time, with the wheel stopped completely at the maximum rotational speed, was approximately 73 seconds. The similarity of the braking time and deceleration pattern was verified through a comparison with the performance test results of the pneumatic brake system applied to the next generation high-speed railway vehicle (HEMU-430X).

• Journal of the Korean Society for Railway
• /
• v.18 no.3
• /
• pp.252-260
• /
• 2015

Ballasted track on an asphalt roadbed can be beneficial for its various effects such as (i) decreasing of roadbed thickness by dispersing train load; (ii) prevention of both strength reduction and weakening in roadbed system by preventing rainwater penetration; and (iii) reducing maintenance cost by preventing roadbed mud-pumping and frostbite. With these beneficial effects, ballasted track on asphalt roadbed has been widely used in Europe and Japan, and relevant research for applying such ballasted track on asphalt roadbed systems in Korea is ongoing. In this study, full-scale static and dynamic train load tests were performed to compare the performance of ballasted track on asphalt roadbed and ballasted track. The optimum thickness levels of asphalt and reinforced roadbeds, corresponding to the design criteria for reinforced roadbed of high-speed railway, was estimated using the FEM program ABAQUS. Test results show that the earth pressure on reinforced roadbed of ballasted track on the asphalt roadbed was relatively low compared with that of simple ballasted track. The elastic and plastic displacements of simple ballasted track on the asphalt roadbed were also lower than those of ballasted track. These test results may indicate that the use of ballasted track on asphalt roadbed is an advantageous system in view of long-term maintenance.

• Structural Monitoring and Maintenance
• /
• v.2 no.1
• /
• pp.77-93
• /
• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.