A clean room is used for adjusting the concentration of suspended particles using an air-conditioner. It has a fan-filter unit combining a centrifugal fan and a high-efficiency particulate air filter that purifies the outside air and directly affects its cleanliness. Defects in these systems are typically detected using special sensors for each fault, which can be costly. Therefore, this paper proposes a system for diagnosing defects in the fan-filter unit using a single differential sensor and deep learning. The fan-filter unit is part of the air-conditioning system, and it is usually defective in bearings, filters, and motors. These faults include ball wear, internal bearing contamination, filter contamination, and motor speed changes. Each defect was artificially induced in experiments, and the differential pressure data of each defect was learned using a long short-term memory (LSTM) deep learning algorithm. The results of deep learning experiments generated by randomly mixing data five times were presented using a confusion matrix, and the results showed an accuracy of 87.2±2.60%. Therefore, the possibility of diagnosing defects in the fan-filter unit using a single sensor was confirmed.

Commercial air-conditioning systems are widely used for buildings of various sizes. Design and installation of these systems follow a certain guideline developed by the manufacturer. The guideline also includes the adequate amount of refrigerant to be charged into the system. However, the guideline is often insufficient to reflect all the characteristics of installation, which results in too little or too much refrigerant. Inadequate amount of refrigerant usually causes more power consumption and reduced air-conditioning / heating capacity. This paper focuses on identifying the relationship between adequate refrigerant amount and various state variables such as condensation temperature of the air-conditioning system. This is based on regression analysis of data obtained through the experiments under controlled temperature and humidity.

The Smart Factory Equipment Engineering System collects and monitors necessary information in real-time. While putting the product into the equipment, operation conditions are lowered through a Recipe Management System. The working conditions are set by Run-to-Run a system for real-time detection and control through Fault Detection Classification function. In this study, the smart factory equipment system associated with the entire system is proposed by defining and integrating the necessary equipment management functions from a smart factory’s point of view. To do this, detailed analysis and process improvement on products, processes, and production line equipment were conducted and implemented in the smart factory equipment engineering system. The models proposed in this paper have been implemented to the production site of BGA-PCB. It has been confirmed that the models have resulted in significant change, and have qualitative and quantitative impacts on the working methods of equipment. Typically, data collection time, data entry time, and manual writing sheets were greatly reduced.