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Fast Calculation Method of Abnormality Degree for Real Time Abnormality Detection in Vehicle Equipment

机译:车辆设备实时异常检测异常程度快速计算方法

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Vibration monitoring is effective for early detection of equipment failure. In the vibration monitoring system proposed in this paper, abnormality detection is performed by applying the nearest neighbor method (NN) to the octave band analysis results of vibration. However, the NN requires a long calculation time and is not suitable for detecting abnormalities in real time. Therefore, applying the One Class Support Vector Machine (OCSVM) to abnormality detection was considered. In this paper, the OCSVM was applied to actual vibration data, and the calculation time was compared with those of the NN. The result shows that the calculation time is significantly reduced compared to the NN approach.
机译:振动监测对于早期检测设备故障有效。在本文提出的振动监测系统中,通过将最近的邻近方法(NN)施加到振动的倍频带分析结果来执行异常检测。然而,NN需要长的计算时间,并且不适合实时检测异常。因此,考虑将一个类支持向量机(OCSVM)应用于异常检测。在本文中,将OCSVM应用于实际振动数据,并将计算时间与NN的计算时间进行比较。结果表明,与NN方法相比,计算时间显着降低。

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