Recently, interest in Prognostics and Health management (PHM) has been increasing as an advanced technology of maintenance. PHM technology is a technology that allows equipment to check its condition and predict failures in advance. To realize PHM technology, it is important to implement artificial intelligence technology that diagnoses failures based on data. Vibration data is often used to diagnose the state of the rotating machine. Additionally, there have been many efforts to convert vibration data into 2D images to apply a convolutional neural network (CNN), which is emerging as a powerful algorithm in the image processing field, to vibration data. In this study, a series of PHM processes for acquiring data from a rotary machine and using it to check the condition of the machine were applied to the rotary table. Additionally, a study was conducted to introduce and compare two methodologies for converting vibration data into 2D images. Finally, a GUI program to implement the PHM process was developed.

Due to recent development of sensor technology and IoT, research is being actively conducted on PHM (Prognostics and Health Management), a methodology that collects equipment or system status information and determines maintenance using diagnosis and prediction techniques. Among various research studies, research on anomaly detection technology that detects abnormalities in assets through data is becoming more important due to the nature of industrial sites where it is difficult to obtain failure data. Conventional machine learning-based and statistical-based models such as PCA, KNN, MD, and iForest involve human intervention in the data preprocessing process. Thus, they are not suitable for time series data. Recently, deep learning-based anomaly detection models with better performances than conventional machine learning models are being developed. In particular, several models with improved performance by fusing time series data with LSTM, AE (Autoencoder), VAE (Variational Auto Encoder), and GAN (Generative Adversarial Network) are attracting attention as anomaly detection models for time series data. In the present study, we present a method that uses Likelihood to improve the evaluation method of existing models.

Recently, many attempts have been made to use microalgae as raw material for next generation biodiesel. Since conventional microalgae detection is performed in the laboratory after collecting algae in the field, it is necessary to develop a portable fluorescence measuring device that can effectively reduce detection duration in the field. In this study, we developed a portable fluorescence measurement device composed of an optical system and a control system to detect microalgae in the field. The optical system stimulates excitation light suitable for algae to be measured and determines the amount of algae by measuring the amount of emitted light through the PMT sensor. The optical system facilitates seamless change of filters and lenses according to kinds of algae. This was validated by checking the amount of light measured according to concentration of CC125. Reliability of the fluorescence measuring device was verified through repeated experiments.

In this study, we developed a 3D chocolate printer and studied the conditions needed for chocolate printing. Because chocolate is a mixture of cocoa mass, cocoa butter and sugar particles, its properties vary with temperature, and care is required in melting and extrusion. A chocolate supply unit is composed of a heating block and a syringe pump. It is integrated with a 3-axis linear robot. In order to be more accurate than the existing 3D chocolate printer is, the system was configured so that the printing line width became 430 μm. Printing performance was studied according to various parameters. The condition needed for printing lines with a stable width was discovered by the experimental design method and has been confirmed by a 2D line test. These 3D printing experiments showed that it was possible to build a 3D shape with an inclination angle of up to 45° without support. Further, chocolate printing of a 3D shape has been successfully verified with the developed system.

In this paper, a novel radial beam coupling model was proposed and the design parameters were studied for the efficient transmission of torque. To develop a high performance radial beam coupling, an analytical way to predict the performance in design phase is required. One of the best ways to estimate the performance of the coupling without manufacturing is to evaluate the stress and torsional stiffness by building a finite element model with a special attention to the radial beam cutting part. For the best results of FEA, the material properties were obtained through testing. To verify the reliability of finite element model, the results of FEA were compared with the experiments. The main design parameters of radial beam cutting width, radial beam cutting depth, and radial beam cutting direction were considered for the performance of radial beam coupling.