流形正则化支持高阶张量机及其在行星齿轮箱半监督故障诊断中的应用
Manifold regularized support higher-order tensor machines for semi-supervised fault diagnosis of planetary gearboxes
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摘要: 本文提出了一种基于流形正则化支持高阶张量机(MRSHTM)的行星齿轮箱半监督故障诊断方法。在MRSHTM中引 入CP分解挖掘张量数据中的内在结构信息,并定义张量逆多元二次核函数(Tensor?IMKF)以构建图拉普拉斯算子,从而更好 地描述张量数据之间的流形结构。针对多分类问题,将一对多(OVR)策略引入MRSHTM中,提出一对多流形正则化支持高 阶张量机(OVR?MRSHTM)模型。利用层次多尺度排列熵(HMPE)提取多通道振动信号的“通道×层次×尺度”三阶张量故 障特征,并输入OVR?MRSHTM中进行自动识别。实验结果表明,所提算法能够在张量空间中实现稀缺标记样本下的行星齿 轮箱智能故障诊断。Abstract: In this study, a novel semi-supervised fault diagnosis of planetary gearboxes based on manifold regularized support high er-order tensor machines (MRSHTM) is proposed. In the MRSHTM, CANDECOMP/PARAFAC (CP) decomposition is intro duced to exploit the intrinsic structural information of tensor data, and tensor-based inverse multiquadric kernel function (Tensor IMKF) is defined to construct a Laplacian operator. The constructed graph matrix can better describe the manifold structure be tween tensor data. Besides, the one-versus-rest (OVR) strategy is introduced into the MRSHTM model for multi-class fault diag nosis of planetary gearboxes. Hierarchical multiscale permutation entropy (HMPE) is adopted to extract the three-order tensor fea tures “channel×hierarchical layer×scale”, and then the extracted HMPE values are fed into OVR-MRSHTM for automatic fault identification. The results suggest that the proposed method can achieve semi-supervised fault diagnosis of planetary gearboxes in tensor space.