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中文摘要：
支持矢量机（Support vector machines，SVM）已经在小样本故障识别中得到了广泛应用。与之对比，由于先验知识获取和非线性识别较难实现，基于贝叶斯概率的故障识别方法应用较少。针对上述问题，提出隐性函数S-压缩贝叶斯故障识别方法（LS-BFR）。LS-BFR以高斯随机过程和贝叶斯概率为基础，以高斯回归作为隐性函数，通过S-压缩对回归输出进行变换使其具有概率意义，利用贝叶斯概率实现故障识别。为提高LS-BFR非线性故障识别效果，引入核函数方法在高维空间进行隐性高斯过程回归，并给出基于贝叶斯参数估计的核函数参数选择方法。在转子试验台上模拟了不对中和不平衡故障，并利用LS-BFR进行故障识别。试验结果表明，基于隐性函数和S-压缩的LS-BFR方法能有效地进行小样本故障识别，且识别效果优于SVM。
英文摘要：
Support vector machines （SVM） are extensively exploited for small sample fault recognition. On the contrary, due to difficulties such as a priori knowledge acquisition and nonlinear recognition, fault recognition approaches based on Bayesian probability have found few applications. To deal with these problems, the latent function sigmoid compression Bayesian fault recognition （LS-BFR） is proposed. Based on Gaussian stochastic process and Bayesian probability, LS-BFR takes Gaussian regression as the latent function, and changes the regression output into probability by sigmoid compression. To improve the performance of LS-BFR for nonlinear fault recognition, kernel functions are introduced to perform latent Gaussian process regression, and a practical approach based on the Bayesian parameter estimation method is proposed to determine kernel function parameters. The performance of LS-BFR is validated by recognizing the faults of misalignment and unbalance simulated on a rotor testbed. Experiment results show that the LS-BFR approach based on latent function and S-compression can effectively perform small sample fault recognition and achieve better fault recognition accuracy than SVM.
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