再生核支持向量机在刀具故障诊断中的应用
Application of Reproducing Kernel Support Vector Machinein Fault Diagnosis of Tool Wea
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摘要: 提出了一种构造再生核的新方法: 用Walsh函数作为空间V0的尺度函数, 构造出L2(R)空间的正交规范序列。首先, 结合小波多分辨分析, 将Hilbert空间分为一系列子空间, 并根据可分Hilbert空间与L2(R)的等价性, 利用内积同构的线性算子, 把V0子空间的尺度函数折算为Hilbert空间的子空间 \widetilde\mathrmV_0的尺度函数, 构造出新的Walsh序列再生核; 然后, 运用小波包频带能量分解技术提取不同频带内刀具在不同工作状态下的特征向量。通过仿真实验表明, 该尺度再生核函数具有更高的辨识精度, 较少支持向量数目, 充分体现了支持向量机较好的推广性能。Abstract: A new reproducing kernel function of least square support vector machines based on Walsh series is presented in this paper. First, the reproducing kernel is constructed in reproducing kernel Hilbert space (RKHS). According to the wavelet multi-resolution analysis, Walsh consequence can be seen as a set of orthogonal basis to construct the reproducing kernel.Second, the features are extracted from different frequency segment with the technology of wavelet packet frequency segment power decomposition, which reflects different working state of the cutting tool, and th ese features are taken as inputs fault of support vector machine (SVM) classifier.The simulation result shows that reproducing kernel SVM can accurately divide the working conditions of a tool.The trained classifier, as fault intelligent classification, has very strong identification capability.