• Journal of Broadcast Engineering
• /
• v.27 no.6
• /
• pp.885-896
• /
• 2022

Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.4
• /
• pp.65-70
• /
• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.2
• /
• pp.221-227
• /
• 2016

The main shaft is one of the key components connecting the rotor hub and gear box of a wind power generator. Typically, main shafts are manufactured by open die forging method. However, the main shaft for large MW class wind generators is designed to be hollow in order to reduce the weight. Additionally, the main shafts are manufactured by a casting process. This study aims to develop a manufacturing process for hollow main shafts by the open die forging method. The design of a forging process for a solid main shaft and hollow shaft was prepared by an open die forging process design scheme. Finite element analyses were performed to obtain the flow stress by a hot compression test at different temperature and strain rates. The control parameters of each forging process, such as temperature and effective strain, were obtained and compared to predict the suitability of the hollow main shaft forging process. Finally, high productivity reflecting material utilization ratio, internal quality, shape, and dimension was verified by the prototypes manufactured by the proposed forging process for hollow main shafts.