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.