基于相对变换的ICA故障检测方法

针对传统独立主元分析方法(independent component analysis,ICA)在标准化处理后导致特征值大小近似相等,难以提取有代表性变量等问题,提出了一种基于相对变换的独立主元分析(relative transformation ICA,RTICA)故障检测方法。该方法引入欧氏距离相对变换理论,将原始空间数据变换得到相对空间,然后在相对空间进行独立主元分析,降低相对空间的数据维数,使提取的独立主元特征具有更大的适应性,建立故障检测模型,最终实现在线故障检测。该方法通过田纳西-伊斯曼过程仿真加以验证,并应用到电主轴裂纹故障的状态监测中,实验结果表明该方法能有效减少独立主元个数,简化故障检测模型的复杂度,增强状态检测性能。

Fault detection method based on relative transformation ICA

Aiming at the problems that the eigenvalues of independent component analysis(ICA) are approximately equal and the representative variables cannot be extracted effectively after dimensionless standardization processing, a fault detection method based on relative-transformation ICA (RTICA) is proposed.The method introduces the relative-transformation based on Euclidean distance.Firstly, the RTICA approach transfers the original data space into relative space by computing the Euclidean distance.Secondly, ICA approach is used to reduce the dimensions of the data in the relative space, extract the independent PCs possess greater adaptability, build the fault detection model and implement on-line fault detection.The proposed method has been applied to Tennessee Eastman (TE) and the fault simulation experiment in electric spindle system.The results show that the method can effectively reduce the number of independent principal components, simplify the complexity of fault detection model, and enhance the performance of state detection.