• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.199-203
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• 2015

With the development of industry these days, the demand for electric power increases and the larger capacity for power transfer is required. The scales of facilities should become larger; and the relative systems are required to operate with a higher degree of reliability. Therefore, stabilization of electric power systems is an important issue. The high degree of reliability required in the process of production and supply of electric power is an essential part of industrial society. Accident such as blackouts causes a hugh amount of economic losses to the high-tech industrial society dependent upon electric power. This paper is about the basic study of the relations between the load current and lifetime of power cables in operation. In order to do the research, we installed a current transformer and an equipment for measuring temperature at the 6.6. kV cables in operation. The two equipments have been installed on the cable systems in operation for the last 20 years. Since the insulation resistance of most of the cables showed the value larger than the threshold, it was not easy to tell the remaining lifetime of cables. The load current of the cables was almost constant. The surrunding temperature was $15{\sim}25^{\circ}C$, little variation of temperature values.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.