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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm
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Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm

机译：基于新型混合聚类算法的旋转机械故障诊断

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摘要

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on the gradient descent technique, and sample weights are computed by using the distribution density function of a sample. Feature weighting and sample weighting highlight the importance of sensitive features and representative samples, and simultaneously weaken the interference of insensitive features and vague samples. The presented algorithm is described and applied to the incipient fault diagnosis of locomotive roller bearings. The diagnosis result demonstrates the superior effectiveness and practicability of the algorithm, and shows that it is a promising approach to the fault diagnosis of rotating machinery.
机译：提出了一种基于三层前馈神经网络（FFNN），分布密度函数和聚类有效性指标的混合聚类算法。在该算法中，考虑了特征加权和样本加权，并由聚类有效性指标自动确定了最佳聚类数。通过基于梯度下降技术的FFNN学习特征权重，并使用样本的分布密度函数计算样本权重。特征加权和样本加权突出了敏感特征和代表性样本的重要性，同时减弱了不敏感特征和模糊样本的干扰。描述了该算法并将其应用于机车滚子轴承的早期故障诊断。诊断结果证明了该算法的优越性和实用性，表明该算法是旋转机械故障诊断的一种有前途的方法。

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