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A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection

机译:基于集体异常检测的新型车辆齿轮箱故障诊断方法

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Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.
机译:针对变速箱故障诊断的问题,我们提出了一种基于集体异常检测的新型半监督方法。基于有限的样本数据,方法的原理是通过使用数据分布作为度量来检测测试数据集是否包含异常模式。遵循意外分布的序列将被识别为集体异常,其可能由故障模式生成。该方法包括三个步骤。首先,使用多变量高斯分布的混合来符合样品数据集和测试数据集的结构。然后,基于最大似然估计算法,我们希望搜索可以使用最高程度的数据分布的最佳参数。最后,固定点迭代算法用于解决可能性估计函数。实验结果表明,该方法可用于在没有先前知识的情况下找到齿轮箱的故障模式。

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