Chang-Seop Song

Quick Wafer Alignment Using Feedforward Neural Networks

This paper proposes a wafer alignment method using a feedforward neural network (FNN). A wafer is placed on a vacuum table and its misalignment is inspected by a frame grabber. The alignment of the wafer is repeatedly corrected by adjusting its kinematic positions until the misalignment becomes zero. The training set is composed of the misalignment and the xy¿ compensation. Fifteen sets of misalignment data were measured by the conventional alignment method. The FNN has four normalized inputs as mark locations in the field of vision and three normalized outputs for the kinematic compensation. The macro and micro steps share the FNN by means of a multiplexer (MUX) and a scaler. The training rule was back-propagation (BP). The proposed method was applied to a wafer manufacturing machine and its alignment performance was compared with that of conventional methods. The alignment time was reduced by the FNN model which was especially efficient in the case of macroscopic alignment.

Pattern Recognition of Rotor Fault Signal Using Bidden Markov Model

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

The Characteristic Analysis of the Load-sensitive Hydraulic Control System for Closed Center Type of a Wheel Loader

In this study, the characteristics of the load-sensitive hydraulic control system for closed center type of a wheel loader were analyzed using developed analysis program based on Amesim tool. From the parametric analysis, the effects of each factor were revealed. Through the simulation with varying parameters, the system parameter effects on the controllable region and the pump pressure and load pressure variations were studied. The results were compared with the experimental ones. The results and discussions of the present paper could aid in the design of a load-sensitive hydraulic control system for closed center type.

A Study on Discrete Hidden Markov Model for Vibration Monitoring and Diagnosis of Turbo Machinery

Condition monitoring is very important in turbo machinery because single failure could cause critical damages to its plant. So, automatic fault recognition has been one of the main research topics in condition monitoring area. We have used a relatively new fault recognition method, Hidden Markov Model(HMM), for mechanical system. It has been widely used in speech recognition, however, its application to fault recognition of mechanical signal has been very limited despite its good potential. In this paper, discrete HMM(DHMM) was used to recognize the faults of rotor system to study its fault recognition ability. We set up a rotor kit under unbalance and oil whirl conditions and sampled vibration signals of two failure conditions. DHMMS of each failure condition were trained using sampled signals. Next, we changed the setup and the rotating speed of the rotor kit. We sampled vibration signals and each DHMM was applied to these sampled data. It was found that DHMMs trained by data of one rotating speed have shown good fault recognition ability in spite of lack of training data, but DHMMs trained by data of four different rotating speeds have shown better robustness.