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.

The high voltage direct current (HVDC) device has been used to transmit electrical power with an advanced technology of semiconductors. The sustainable energy generation technologies of solar power and windmills are demanding that the HVDCs have high performance and reliability. In this regard, the cooling performance of the HVDC becomes a significant research topic because the temperature increase affects the operation of the device. The evaluation system to assess the cooling performance has been developed and is proposed in this paper. The experimental apparatus is presented in detail. Our experiments have shown the accuracy of flow rates, pressure drops, and the temperatures in the desired measurement points. We have successfully developed an evaluation system of the cooling performance of the HVDC device which has 2.48 kW of heat dissipation.