Condition monitoring using compressive measurement with variance considered machine algorithm



Jun Young Jeon Myung Jun Lee Gyuhae Park To kang Soon Woo Han


In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and the improved damage detection capability. A built-in rotating system was used for demonstration. Data were then compressively sampled to obtain compressed measurement. For damage detection, Variance considered machine (VCM) algorithm was employed to classify failure modes of rotating systems. The experimental results showed that the proposed method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

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Regular Session Papers