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A method of fault detection on diesel engine based on EMD-fractal dimension and fuzzy C-mean clustering algorithm

机译：基于EMD分形和模糊C均值聚类算法的柴油机故障检测方法。

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摘要

For the non-stationary characteristics of vibration signal and fuzzy characteristics of feature parameter, a method based on EMD-fractal dimension and FCM is proposed for feature extraction and pattern recognition of diesel engine mechanical fault. Firstly decompose vibration signal by EMD, choose IMFs can reflect fault characteristic information better according to the correlation factor, and compute fractal dimension of the selected IMFs as feature vector, which is used as input of FCM after standardization. The optimized classified matrix and clustering centers are obtained. By calculating the nearness degree between the unknown-fault samples and the known-fault ones, the fault pattern is identified at last. The experimental results express that this method can diagnose faults of the crank-shaft bearing of diesel effectively.
机译：针对振动信号的非平稳特性和特征参数的模糊特性，提出了一种基于EMD-分形维数和FCM的柴油机机械故障特征提取与模式识别方法。首先用EMD分解振动信号，选择IMFs可以根据相关因子更好地反映故障特征信息，并计算出所选IMFs的分形维数作为特征向量，标准化后作为FCM的输入。获得了优化的分类矩阵和聚类中心。通过计算未知故障样本与已知故障样本之间的接近度，最终确定出故障模式。实验结果表明，该方法可以有效地诊断柴油机曲轴轴承的故障。

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