The method uses wavelet transform and principle component analysis to preprocess fault signal, afterward training and testing wavelet neural network with the preprocessed fault characteristic data.
该方法首先利用小波变换和主成分分析对故障信号进行预处理，然后用处理后的故障特征数据对小波神经网络进行训练和测试。
Aiming at the preceding problem, this paper puts forward a feature selection method using Information Gain (IG) and Principle Component (Analysis) (PCA).
针对上述问题，提出了信息增益(IG)与主成分分析(PCA)相结合的特征选择方法。
The paper briefly introduced the theoretical foundation of MSP method, which include Principle Component Analysis (PCA), Principle Component Regression (PCR), and Partial Least Squares (PLS).
介绍了多变量统计投影方法的主要理论基础，包括：主元分析(PCA)、主元回归(PCR)、偏最小二乘(PLS)。
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