参照化流形空间融合学习的敏感特征提取与异常检测方法

针对遥测振动信号冲击强、响应周期短、共振频带宽和小样本等特点导致异类模式识别率低的问题,提出基于参照化流形空间融合学习的敏感特征提取与异常检测方法。采用多尺度分析方法将信号正交无遗漏地分解到各尺度带中,提取多尺度特征构造高维特征集；以相同的正常信号样本结合相同类型的异常样本建立专属参照化模型单元,采用线性流形学习获取各参照化模型单元多尺度流形特征差异,增强异常特征的敏感性。融合各参照化模型单元的投影矩阵对原始特征集进行升维再学习,获取低维多尺度敏感流形特征；输入到分类器实现对未知样本状态辨识。实测信号处理结果验证了算法的有效性。

Sensitive feature extraction and anomaly detection method based on referenced manifold spatial fusion learning

Aiming at the problem of low recognition rate of heterogeneous patterns caused by the characteristics of small sample size, strong impact, short response period and wide resonance frequency bandwidth of telemetry vibration signals, a method for sensitive feature extraction and anomaly detection of telemetry vibration signal based on referenced manifold spatial fusion learning was proposed. The multi-scale analysis method was used to decompose the signals orthogonally into each in the scale band; the multi-scale feature was extracted to construct the high-dimensional feature set. The same normal signal sample was combined with the same type of abnormal sample to establish the exclusive reference model unit, and the linear manifold learning was used to obtain the multi-scale manifold feature difference of each reference model unit to enhance the sensitivity of anomalous features. The projection matrix of each reference model unit was used to enhance the original feature set and obtain the low-dimensional multi-scale sensitive manifold feature. The input to the classifier was used to realize the state recognition of the unknown sample. The measured signal processing results verified the effectiveness of the algorithm.