• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.51-57
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• 2010

Steganography techniques can be used to hide data within digital images with little or no visible change in the perceived appearance of the image. I propose a steganalysis to detecting hidden message in sequential steganography. This paper presents adjusted technique for detecting abrupt jumps in the statistics of the stego signal during steganalysis. The repeated statistical test based on CUSUM-SPRT runs constantly until it reaches decision. In this paper, I deal with a new and improved statistic $g_t$ by computing $S^{t^*}_j$.