Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis
Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis
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DOI：
10.1088/0957-0233/22/2/025102
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发表时间：
2011-02-01
影响因子：
2.4
通讯作者：
Miao, Qiang
中科院分区：
文献类型：
--
作者：
Wang, Dong;Tse, Peter W.;Miao, Qiang
通过支持向量数据描述融合多个健康指标，开发了一种增强齿轮箱故障诊断和预测的新方法。首先，使用康布莱特变换从原始信号中识别齿轮残差信号。其次，根据齿轮残差信号的观察，共识别出11个齿轮健康指标，并分为两类指标。第一类指标和第二类指标分别用于故障诊断和预测。第一类有六个指示器，对异常影响触发的脉冲信号敏感。第二类有五个指标，适合跟踪故障的退化情况。第三，通过支持向量数据描述，将前6个健康指标融合为一类指标进行故障诊断。其余5个指标融合为二类指标，用于故障预测。最后，设计了高斯核，通过宽度尺寸的最佳范围来增强一类和二类指标的性能。通过实验验证了所提方法的有效性。新方法已被证明优于单独使用未融合指标的方法。
A novel method for enhancing gearbox fault diagnosis and prognosis is developed by fusion of multiple health indicators through support vector data description. First, the Comblet transform is used to identify gear residual error signals from the raw signal. Second, based on the observation of gear residual error signals, a total of 11 gear health indicators are identified, and are categorized into two types of indicators. The first and second types of indicators are for fault diagnosis and prognosis, respectively. The first type has six indicators, which are sensitive to impulsive signals triggered by anomalous impacts. The second type has five indicators, which are suitable for tracking degradation of faults. Third, through the support vector data description, the first six health indicators are fused into type one indicators for fault diagnosis. The remaining five indicators are fused into type two indicators for fault prognosis. Finally, a Gaussian kernel is designed to enhance the performance of type one and two indicators by optimal range of width size. The effectiveness of the proposed method is validated through experiments. The new method has been proven to be superior to methods that use unfused indicators individually.